Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition)

Introduction

Hepatocellular carcinoma (HCC), the fifth most prevalent malignancy in China, accounts for approximately 400,000 new cases annually (1). It ranks as the second leading cause of cancer-related mortality, surpassed only by lung cancer. Despite advancements in therapeutic strategies, surgical resection remains the cornerstone of HCC management in China. However, postoperative recurrence rates within 5 years alarmingly reach up to 50–70% (2,3), while recurrence after liver transplantation ranges from 4.3% to 57.8% (4-7). The propensity of HCC to invade blood vessels and thereby facilitate hematogenous spread is a pivotal factor contributing to its recurrence and metastasis. A critical area of concern is microvascular invasion (MVI), characterized by the presence of cancer cell nests within the lumens of small, endothelium-lined vessels that are detectable solely under microscopy. These nests are predominantly located within the tumor capsule and portal vein branches adjacent to the carcinoma. Emerging evidence underscores MVI’s role as an independent prognostic factor for HCC recurrence and metastasis, highlighting its biological significance and impact on patient outcomes. Given the ongoing debates surrounding MVI’s diagnostic criteria, classification, prognostication, and therapeutic approaches, the Chinese Association of Liver Cancer has taken a pivotal step. Leveraging the substantial body of evidence-based medicine, particularly contributions from Chinese scholars in the realm of treating HCC patients with MVI, the committee convened a multidisciplinary panel of experts. Through extensive deliberations, they formulated the “Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition)”. This document aims to guide clinical practice by synthesizing current knowledge and expert opinions on managing this complex condition. As the landscape of evidence-based medicine evolves, this consensus will undergo periodic revisions to incorporate the latest research findings, ensuring its relevance and utility in improving patient care outcomes in HCC management.

Methods

This study comprised three stages, including: (I) systematic literature reviews to identify knowledge gaps and support consensus statements; (II) a modified Delphi method to develop evidence- and expert-based guidelines; (III) finalization of the clinical consensus statements based on recommendations from a panel of experts.

PubMed, Cochrane Library and Embase databases through May 31, 2023, to identify knowledge gaps in clinical guidelines, clinical consensus statements, and systematic reviews on diagnosis, prediction and treatment of MVI. In a modified Delphi process from July 2023 through December 2023, experts participated in three consecutive surveys (including two web-based surveys).

The first survey aimed to identify knowledge gaps and clinical presence needs in the definition, classification, prediction and treatment strategies of MVI in our country. Expert feedback was solicited through open-ended questions and “yes/no” questions asking about agreement with consensus statements. In the second survey, experts received literature specific to the revised consensus statements and indicated whether they agreed (“yes”/“no”) with each statement. Per Delphi protocol, consensus for each statement was defined as having agreement from at least 80% of experts. Lastly, experts assigned proposed recommendation strengths to each consensus statement. Statement-specific consensus was considered favorable when at least 80% of panelists rated the recommendation strength of the statement as strong, week or good practice statement (GPS).

The evidence grading in this consensus follows the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system (Table 1), adhering to the guidelines for evidence evaluation and recommendation grading as outlined by the GRADE Working Group (http://www.gradeworkinggroup.org/) and the “Oxford Centre for Evidence-based Medicine 2011 Levels of Evidence” (8,9). The strength of the expert recommendations is based on the GRADE system’s guidance for grading recommendations. This consensus has been registered on the International Practice Guidelines Registry and Transparency Platform (http://www.guidelines-registry.cn/), with the registration number PREPARE-2024CN185.

Results

Systematic literature review and survey results

The literature reviews identified 251 publications included in the qualitative synthesis. The first survey consisted of 12 questions and statements, which were modified in the second survey and finally summed up to eight statements in the third survey. All eight statements had favorable consensus. Experts expressed agreement on seven statements that received strong or moderate recommendations; one statement received at least 1 weak recommendation (Table S1).

Recommendations

Diagnosis and classification of MVI

Incidence, pathological diagnosis, and classification

The incidence of MVI in HCC ranges from 17.0% to 93.4% (10-21). The incidence of MVI is positively correlated with tumor diameter in HCCs. It ranges from 17.0–40.6% in tumors less than 3 cm (16,18,21-26), 31.0–42.5% in those between 3–5 cm (22,23,27,28), and 46.2–93.4% in HCCs larger than 5 cm (23,27,29-33). For multifocal HCCs, the incidence of MVI varies between 9.9% and 81.3% (4,13,17,23,34-37). Additionally, the incidence of MVI in HCCs meeting the Milan criteria is reported to be between 9.9% and 54.0% (4,13,38,39).

Certain pathological features of MVI, such as the number of invading microvessels (40-43), their distribution (43-45), intranodular cancer cell count (40,43,46), invasive typing (42), and invasion of microvessels with a muscle layer (44), significantly affect the prognosis of patients with HCC. Roayaie et al. (44) defined MVI as tumor cells visible in microvessels under a microscope. They then classified MVI into three classes based on two risk factors: a muscle layer in the invaded microvessels and a distance from the tumor body ≥1 cm. The three categories of MVI are B1 class: MVI present without other risk factors; B2 class: MVI with one risk factor; and B3 class: MVI with two risk factors. Other researchers (38,46,47) have defined MVI as the microscopic discovery of HCC invading one or more of the portal veins, hepatic vein, microartery, or lymphatic vessels. Iguchi et al. (46) proposed a classification of MVI based on intranodular cancer cell count and the number of MVIs, with two main types (low risk: MVI present without other risk factors; high risk: MVI number ≥2 or intranodular cancer cell count ≥50). Feng et al. (42) differentiated MVI into noninvasive (free and adherent types) and invasive (invasive and breakthrough types) MVI, and with further refinement based on the number of MVIs, they classified MVI into Type I (noninvasive MVI and number <5), Type II (invasive MVI or number >5), and Type III (invasive MVI and number >5). However, current MVI classifications are based on nonobjective indicators and still need to be validated by multicenter large-sample studies.

This consensus recommends the adoption of the pathological grading standard for MVI from the “Guidelines for the Pathological Diagnosis of Primary Liver Cancer” (10): M0: no MVI found; M1: ≤5 MVIs occurring in the peri-tumoral liver tissue area (≤1 cm); M2: >5 MVIs or MVI occurring in the distal peri-tumoral liver tissue area (>1 cm). Recent studies (20,48) have indicated that differentiating MVI according to this standard can be used to assess the prognosis of early-stage patients with HCC more accurately. The median overall survival (OS) for patients with M0, M1, and M2 disease was 61.1, 52.7, and 27.4 months (P<0.001), respectively, and the median disease-free survival (DFS) was 43.0, 29.1, and 13.1 months (P<0.001), respectively.

Recommendation 1: it is recommended to use the MVI pathological grading criteria as outlined in the “Guidelines for Pathological Diagnosis of Primary Liver Cancer”: M0: No MVI found; M1: ≤5 MVI and occurring in the peri-tumoral liver tissue area (≤1 cm); M2: >5 MVI or MVI occurring in the distant peri-tumoral liver tissue area (>1 cm) (evidence level: C; recommendation level: strong).

Specimen sampling methods

Currently, the common methods for sampling liver cancer resection specimens include the 3-point method, 7-point method, 13-point method, and image-matching digital macro-slides (IDSs) (Figure 1). The Eastern Hepatobiliary Surgery Hospital (EHBH) (20) reported that the “7-point” method for diagnosing MVI could yield a 47.1% detection rate, which is comparable to the “13-point” method with a 51.3% detection rate (P=0.517). Both methods are significantly superior to the “3-point” method, which has a detection rate of 34.5% (P=0.048). Moreover, the “7-point” method is simpler and more practical than the “13-point” method.

Figure 1 Pathological examination on specimen collection. (A) 3-point method; (B) 7-point method; (C) 13-point method; (D) IDS. IDS, image-matching digital macro-slide.

However, another study (45) suggested that to improve the detection rate of MVI, the number of sampling points in the tissue adjacent to the cancer should not be fixed but positively correlated with the tumor diameter and number. The authors noted that for tumors with diameters of 1–3 cm, 3–5 cm, or greater than 5 cm, as well as for multiple tumors, the number of sampling points should reach at least 4, 6, 8, or 8, respectively; however, this recommendation still requires further research for validation. IDS refers to the process of slicing the entire liver cancer resection specimen, then further utilizing whole slide imaging (WSI) technology to obtain high-quality visual digital images, and finally analyzing and diagnosing the images, including pathological features of liver cancer such as MVI, using software that matches the scanner. It has been reported (49) that IDS can significantly improve the detection rate of MVI compared to the traditional “7-point” method, but IDS requires high equipment standards and involves a substantial workload in slide reading. With the current immature state of artificial intelligence (AI) diagnosis, its widespread application is limited (49).

Recommendation 2: it is recommended to use the “7-point method” for sampling liver cancer specimens (evidence level: B; recommendation level: strong), and IDS may be used where feasible (evidence level: C; recommendation level: strong).

Clinical typing

Clinical typing, which combines pathological typing and clinical characteristics, can not only more accurately predict the prognosis of patients with liver cancer with MVI but also refine the clinical treatment. Zhang et al. (50) constructed nomograms using variables such as alpha-fetoprotein (AFP), tumor capsule, tumor diameter, hepatitis B e-antigen (HBeAg), hepatitis B virus deoxyribonucleic acid (HBV-DNA), tumor number, and esophageal/gastric varices to predict the clinical prognosis of MVI-positive HCC patients. The EHBH reported (51) that the prognosis of HCC patients is significantly related to AFP, the presence of cirrhosis, tumor number, tumor diameter, MVI number, and distribution. Nomograms constructed using these factors, by calculating scores, can classify patients into three types. This clinical typing can accurately predict patient prognosis and only class C patients can benefit from postoperative adjuvant transarterial chemoembolization (TACE). However, the generalizability of this clinical typing still needs further validation.

MVI prediction

MVI is currently the most important pathological feature that can guide the prevention and treatment of HCC recurrence. The diagnosis of MVI relies on histopathological examination of postoperative resection specimens rather than preoperative biopsy tissues. Due to the delayed results of postoperative pathological examinations, MVI information cannot guide preoperative and intraoperative treatment decisions. This highlights the necessity and importance of predicting MVI before surgery.

Clinical feature prediction

The advantage of using clinical features for predicting MVI lies in its easy application and rapid dissemination once successful. Common clinical features related to MVI prediction include age (52-57), gender (58-60), tumor diameter (61-68), number of tumors (61,67,69-74), tumor volume (12,75,76), tumor location (77), tumor morphology (26,78-81), degree of liver cirrhosis (56,82-86), portal hypertension (63,87,88), HBeAg (55,89), HBV-DNA (63,71,87,90,91), AFP (61,66,76,92-97), AFP-L3 (98-101), Des-gamma-carboxyprothrombin (DCP) (66,76,81,95,102-105), bilirubin level (96,106-109), alkaline phosphatase (ALP) (62,81), alanine aminotransferase (ALT) (59,110,111), aspartate aminotransferase (AST) (62,66,79,112), gamma-glutamyl transferase (GGT) (70,113-116), lactate dehydrogenase (LDH) (70), alpha-L-fucosidase (AFU) (86), neutrophil count (74,117), platelet count (90), lymphocyte count (106), hemoglobin (118), prothrombin time (PT) (59,119), international normalized ratio (INR) (19,53), C-reactive protein (59) and serum amyloid A (120), among others. Other clinical features included pathological characteristics (tumor differentiation (67,121-124), completeness of the tumor capsule (37,83,87,125), capsule invasion (26,126), satellite nodules (40,94,127), etc., the AST/ALT ratio (66), the neutrophil count/monocyte count (NLR) (62,128,129), the ALP/lymphocyte count (ALR) (130), the platelet count/lymphocyte count (PLR) (131,132), the GGT/lymphocyte count (GLR) (117,133), the GGT/platelet count (GPR) (117), the lymphocyte count/monocyte count (LMR) (54), the body mass index (BMI) (134,135), and the circulating tumor cell count (136). Pathological characteristics are primarily used for predicting MVI in liver tumor biopsies and serve as supplements for the missed detection of MVI by classic sampling methods.

It has been reported that the accuracy of single clinical feature prediction of MVI is not ideal. For example, the sensitivity of AFP for predicting MVI ranges from 44% to 64%, and the specificity ranges from 66% to 82% (112,137,138). The sensitivity of DCP for predicting MVI was 70%, with a specificity of 63%. The sensitivity and specificity of tumor diameter for predicting MVI were 50.0% and 70.9%, respectively (138). Therefore, clinical features are often used as one of the components of MVI prediction models and are not recommended for the independent prediction of MVI. For example, the combination of AFP, DCP, and tumor diameter for predicting MVI achieved an area under the curve (AUC) of 0.74 (95,102,104).

Imaging prediction

Imaging prediction has been the fastest-progressing area in predicting MVI in recent years. In particular, the introduction of radiomics has led to more accurate MVI prediction than image features, making it one of the most promising directions for MVI prediction development. Commonly used imaging features for predicting MVI include whether the tumor capsule is intact or invasive (65,78,81,90,109,110,139), whether the tumor margin is smooth (86,128,139-144), whether there is enhancement around the tumor (58,92,93,106,128,144-146), enhancement patterns (24,90,124,147,148), intratumoral artery visibility (69,106,139,144,149-151), lack of blood supply (152), and magnetic resonance imaging (MRI)-specific apparent diffusion coefficient (ADC) (22,65,110,153-156), peri-tumoral low density in the hepatobiliary phase (65,69,78,92,140,145,148,157), D-value (143,144,155,156,158,159), T1 relaxation time (154), T2 peri-tumoral low signal (24), MRI elastography characteristics (146,160,161), MRI-restricted spectrum imaging features (162), computed tomography (CT)-specific arterial phase CT value (163), ultrasound-specific high aspect ratio (164), minimum grayscale value (164), grayscale ratio (124), ultrasound elastography characteristics (165), positron emission tomography (PET)-CT-specific standardized uptake value (SUV) (100,166-171), etc. Previous studies indicated that single imaging features, such as tumor margin fuzziness or irregularity (sensitivity: 66–73%; specificity: 61–86%) (172,173), peri-tumoral low density (sensitivity: 43–55%; specificity: 87–90%) (145,173), arterial phase peri-tumoral enhancement (sensitivity: 59%; specificity: 80%) (145), ADC (sensitivity: 73%; specificity: 70%) (174), and PET-CT SUV values (sensitivity: 67%; specificity: 80%) (170), still cannot efficiently predict MVI. Therefore, combining indicators for MVI prediction is recommended.

Since applying radiomics for MVI prediction, its development has been rapid. Several meta-analyses have estimated the ability of different imaging methods to predict MVI. CT radiomics predicts MVI with a sensitivity and specificity of 82% and 79%, respectively, and an AUC of 0.85–0.87 (175,176); MRI radiomics predicts MVI with a sensitivity and specificity of 79% and 81%, respectively, and an AUC of 0.87 (175,177); and ultrasound radiomics predicts MVI with a sensitivity and specificity of 79% and 70%, respectively, and an AUC of 0.74–0.81 (175,178), of which the predictive efficiency of ultrasound radiomics based on the original radio frequency (RF) signal is superior to that based on grayscale images (179). However, these studies were limited by small sample sizes, lack of external validation, and nonuniform inclusion criteria. Further large-scale validation of their predictive efficiency is necessary. Additionally, deep learning technology has also been preliminarily applied in the field of MVI prediction. Several studies have reported (180-188) AUCs ranging from 0.74 to 0.98, with the predictive accuracy peaking at 99.1%. Nonetheless, this technology still requires large-scale validation to confirm its predictive efficiency.

Comprehensive prediction methods

The combination of multiple factors could enhance the accuracy of MVI prediction over the use of a single factor. Lei et al. collected data from 1,004 early-stage HCC patients to train and evaluate an MVI predictive model, the EHBH nomogram, which included seven factors: tumor diameter, number of tumors, tumor capsule, AFP levels, platelet count, HBV DNA levels, and typical imaging enhancement features. At an optimal threshold of 200, the sensitivity, specificity and AUC were 73.5%, 76.6% and 0.81, respectively (90). Lee et al. (189) reported that using a combination of AFP levels, DCP levels, peritumoral enhancement, and peritumoral low density to predict the occurrence of MVI in patients with HCC <3 cm yielded an AUC, sensitivity, and specificity of 0.87, 65.2%, and 85.9%, respectively. Researchers used 377 patients from two centers to train and evaluate this model. Furthermore, West China Hospital (66) reported a study including 2,160 patients in which the combination of AFP levels, DCP levels, tumor diameter, satellite nodules, AST, and the AST/ALT ratio was used to predict MVI, with an AUC of 0.80. Another study (190) compared the above prediction methods and reported that the EHBH nomogram is the most stable and reliable among all current prediction methods.

Recommendation 3: it is recommended that HCC patients undergo preoperative MVI prediction, and using the EHBH nomogram for predicting MVI is highly recommended (evidence level: C; recommendation strength: weak).

MVI-guided treatment

HCC treatment with MVI follows two main principles: ensuring safety and improving oncological outcomes. Under the premise of ensuring treatment safety, based on the tumor situation and MVI information, the first treatment should include methods that are more effective for treating the primary lesion and MVI. Multidisciplinary comprehensive treatment is needed to reduce recurrence rates, prolong survival time, and improve quality of life.

First curative treatment

For the first treatment of HCC patients with MVI, few studies have compared the efficacy of different treatment methods, and there is a lack of prospective studies. Retrospective studies have shown that for patients with small HCC (diameter ≤3 cm) predicted preoperatively to be at high risk for MVI, liver resection, especially anatomical liver resection, has significantly better 5-year DFS and OS than percutaneous radiofrequency ablation (PRFA)/percutaneous microwave coagulation therapy (PMCT); for low-risk patients, the efficacy of liver resection is similar to that of PRFA/PMCT (16,26,189,191). For patients with HCC meeting the Milan criteria, the long-term prognosis of patients who underwent liver transplantation was better for MVI-positive patients than for those who underwent liver resection, while there was no significant difference in prognosis between patients who underwent anatomical liver resection and those who underwent liver transplantation for MVI-negative patients (192,193). However, another study (194) reported that compared to liver resection, liver transplantation does not provide a survival benefit for patients with resectable HCC with MVI, benefiting only MVI-negative patients. The exploration of MVI prediction is immature, and more clinical trials are needed to improve the ability of MVI to accurately guide early HCC treatment decisions.

Recommendation 4: for patients with resectable HCC, surgical resection is recommended as the first choice (evidence level: C; recommendation strength: strong); for patients with a diameter ≤3 cm who are predicted to be at low risk for MVI, either surgery or PRFA/PMCT can be chosen (evidence level: C; recommendation strength: weak).

Postoperative recurrence prevention

(I) Preoperative neoadjuvant therapy

It has been shown (195,196) that the degree of tumor necrosis induced by preoperative neoadjuvant TACE is negatively correlated with the detection rate of MVI in postoperative pathological examinations. A study from the EHBH (196) showed that in tumor resection specimens with more than 90% necrosis after TACE, the detection rate of MVI was lower than that in the control group that did not undergo TACE before surgery (8.1% vs. 34.2%, P<0.05); for those with 60–90% necrosis, the detection rates between the two groups were similar (47.1% vs. 43.3%, P>0.05); and for those with less than 60% necrosis, the detection rate in the TACE group was significantly greater than that in the control group (88.0% vs. 48.0%, P<0.05). Overall, neoadjuvant TACE before liver resection does not reduce the overall incidence of MVI or improve prognosis (196,197). Kim et al. (198) provided neoadjuvant TACE to patients who underwent liver transplantation and met the Milan criteria and showed that this treatment did not reduce the incidence of MVI or improve long-term survival. Currently, studies on the efficacy of neoadjuvant TACE for patients who are preoperatively predicted to be at high risk for MVI are scarce. A randomized controlled study from the EHBH showed that preoperative radiotherapy does not reduce the postoperative recurrence rate or prolong OS time in high-risk MVI patients (199), but this study had a small sample size, and the results still need to be verified by larger prospective studies.

Recommendation 5: it is not currently recommended to use TACE or radiotherapy alone as neoadjuvant therapy for HCC patients who are predicted to be at high risk for MVI (evidence level: C; recommendation strength: weak); to reduce postoperative recurrence rates, it is encouraged to conduct clinical trials of neoadjuvant therapy (GPS) in these patients.

(II) Intraoperative treatment

Compared with nonanatomic liver resection, anatomic liver resection offers superior long-term therapeutic effects for patients with pathologically MVI-positive HCC (26,92,115,119,200-209), and the efficacy of laparoscopic hepatectomy is like that of open hepatectomy (210). On the other hand, MVI-negative patients do not benefit from anatomic liver resection (206-208). In patients who undergo hepatectomy, in addition to the type of surgery, the width of the margin is also closely related to prognosis. Several studies have demonstrated significantly prolonged DFS and OS in patients with wide margin (margin distance ≥1 cm) resection (204,209,211-214). This prognostic value is even more important than that of anatomical liver resection (28,215-218), but whether MVI-negative patients could benefit from wide-margin resection remains controversial (81,213,214,218). Additionally, a prospective phase II clinical trial reported (219) that intraoperative radiotherapy significantly improved postoperative DFS and OS for patients with centrally located HCC with MVI and narrow margins, while this phenomenon was not observed in MVI-negative patients. Tumor intrahepatic dissemination and metastasis caused by MVI are major causes of death in patients. To ensure surgical safety, it is advisable to minimize and alleviate compression and flipping of the liver and tumor during surgery and to perform in situ resection to reduce and avoid the risk of intrahepatic dissemination. Preemptive ligation or blocking of corresponding watershed vessels also helps to reduce this risk.

Recommendation 6: it is recommended to perform anatomic liver resection or ensure a margin distance ≥1 cm for HCC patients predicted to be at high risk for MVI (evidence level: C; recommendation strength: strong); if anatomic liver resection is not possible or a margin distance ≥1 cm cannot be ensured, intraoperative radiotherapy may be used in equipped centers (evidence level: C; recommendation strength: weak).

(III) Postoperative adjuvant therapy

It has been reported that adjuvant therapy for HCC patients with MVI mainly includes the following measures:

(i) Antiviral therapy
For MVI-positive and hepatitis B-related HCC patients, standardized antiviral therapy can both protect liver function and reduce the recurrence rate, thereby improving long-term survival. Preoperative antiviral therapy can significantly reduce the incidence of MVI (91,220,221), and continuous postoperative antiviral therapy significantly prolongs DFS and OS (209,220,222-225). The commonly used anti-HBV nucleos(t)ide analogs include entecavir, tenofovir disoproxil fumarate, and tenofovir alafenamide.

(ii) Adjuvant TACE (A-TACE)
TACE, which has a short treatment cycle and high safety, is the most used postoperative adjuvant treatment for liver cancer in China. Several studies (209,226-230) have reported that TACE can reduce the recurrence rate and prolong the survival time of HCC patients with MVI, but it does not have this effect on MVI-negative patients (231,232). Additionally, two cycles of A-TACE appeared to have better efficacy than one cycle (233), but the reliability of this result still needs further confirmation. Additionally, only class C patients in the Eastern Hepatobiliary MVI clinical classification group benefitted from A-TACE, and whether class A and B patients benefit still requires further research (51).

(iii) Adjuvant radiotherapy
Two prospective studies have shown that postoperative adjuvant radiotherapy significantly prolongs the DFS and OS of HCC patients with MVI (234,235), especially for those with narrow margins. Retrospective studies (236,237) indicated that postoperative adjuvant radiotherapy is superior to A-TACE, but there is limited research evidence in this area. The recommended technique for postoperative radiotherapy for HCC patients with MVI is conventional fractionated intensity-modulated radiation therapy, with a total dose of 50–60 Gy, and the target area should include 1.0–2.0 cm of liver parenchyma beside the surgical margin.

(iv) Adjuvant hepatic arterial infusion chemotherapy (HAIC)
A multicenter randomized controlled study in China that included 315 patients with HCC and MVI showed (238) that postoperative adjuvant HAIC based on the FOLFOX chemotherapy regimen significantly reduced the recurrence rate compared to the control group. Another meta-analysis (239) of 11 studies including 1,290 patients showed that postoperative adjuvant HAIC can benefit HCC patients in MVI subgroups.

(v) Postoperative targeted therapy or targeted combined immunotherapy
Although the STORM trial (240) failed to prove that sorafenib could reduce postoperative recurrence of liver cancer, it has been reported that postoperative sorafenib (241-243) or lenvatinib (244,245) can benefit HCC patients with MVI. The IMbrave050 study (246) of adjuvant treatment with atezolizumab combined with bevacizumab (T+A) reached its primary endpoint for DFS in the interim analysis, showing a 28% reduction in the risk of recurrence/distant metastasis or death [hazard ratio (HR) for DFS =0.72], suggesting that the T+A regimen could be an effective adjuvant treatment for HCC patients with MVI. A prospective randomized controlled study by the EHBH, which included 198 patients with HCC and MVI (247), reached its prespecified endpoint; compared with active monitoring, sintilimab significantly prolonged DFS (27.7 vs. 15.5 months, P<0.001), but further follow-up is required to confirm the difference in OS. Additionally, the results from several phase II or III clinical studies on preventing postoperative recurrence of liver cancer with targeted and immunotherapy combinations are pending. Before choosing targeted therapy and immunotherapy, it is necessary to assess the patient’s general physical condition, liver function status, treatment risk, etc. Recommendations for baseline assessment and adverse reaction management refer to the “Chinese Expert Consensus on Immunotherapy for Hepatocellular Carcinoma (2021)” (248).

(vi) Postoperative local treatment combined with systemic treatment
Postoperative combined adjuvant therapy may be superior to single-agent adjuvant treatment. For instance, TACE combined with antiviral therapy (222) is superior to using antiviral therapy or TACE alone; TACE combined with sorafenib is superior to using sorafenib or TACE alone (249). However, all the above are retrospective studies. More solid evidence is needed in the future.

(vii) Post-liver transplantation adjuvant therapy
For MVI-positive liver cancer patients undergoing liver transplantation, in addition to routine early adjustment of the immunosuppressant regimen, it has been reported that therapeutic measures to prevent the recurrence of liver cancer with MVI after transplantation include systemic chemotherapy (250) and plasma exchange (251), but prospective studies are still needed to further clarify their efficacy.

Recommendation 7: postoperative antiviral therapy is recommended for hepatitis B-related HCC patients (evidence level: A; recommendation level: strong), and for patients diagnosed with MVI postoperatively, at least one of the following adjuvant treatments are recommended: T+A (evidence level: A; recommendation level: strong), sintilimab (evidence level: A; recommendation level: strong), TACE (evidence level: B; recommendation level: strong), radiotherapy (evidence level: B; recommendation level: weak), HAIC (evidence level: B; recommendation level: weak), lenvatinib or sorafenib (evidence level: C; recommendation level: weak).

Treatment after recurrence

It has been reported (252) that for MVI-positive HCC recurrence in Barcelona Clinic Liver Cancer (BCLC) stages 0/A, the efficacy of resection and radiofrequency ablation (RFA) is superior to that of TACE, but for stages B/C, the efficacy of the three treatments is similar. According to the “Multidisciplinary management of recurrent and metastatic hepatocellular carcinoma after resection: an international expert consensus” (253), and considering the consensus committee’s discussion, this consensus recommends that even if the prognosis for MVI-positive HCC recurrence is poor, PRFA/PMCT should be the first choice if indications are met; if the recurrence focus is solitary, there is no portal vein main trunk cancer thrombus, and if the time to recurrence (TTR) is ≥1 year, surgical resection may be considered. Moreover, the efficacy of comprehensive therapy for recurrent foci is superior to monotherapy. For example, TACE combined with RFA (254,255) or sorafenib combined with RFA (256) is superior to RFA alone in treating recurrent small HCC patients with MVI-positive primary tumors. Additionally, TACE combined with sorafenib is superior to TACE alone (257) in treating recurrent unresectable liver cancer with MVI-positive primary tumors. Therefore, treatment for MVI-positive HCC recurrence needs to be performed with caution, taking into consideration the location of the tumor recurrence, TTR, number, and distribution of recurrent lesions. It is recommended that the appropriate treatment plan be selected after discussion by a multidisciplinary team (MDT).

Recommendation 8: it is recommended to decide on the prevention and treatment plan for MVI-positive HCC recurrence after MDT discussion: if there are ≤3 recurrences and the maximum diameter is ≤3 cm, PRFA/PMCT is preferred; if the recurrence focus is solitary, there is no portal vein main trunk cancer thrombus, and TTR ≥1-year, surgical resection may be considered; for unresectable recurrence foci or early recurrence (TTR ≤1 year) and PMCT/PRFA cannot be performed, TACE is recommended (evidence level: C; recommendation level: strong).

Multidisciplinary diagnosis and treatment process

Through multidisciplinary collaboration, MDTs leverage the professional strengths of each discipline to maximize patient benefits, especially for the diagnosis and treatment of HCC patients with MVI, which necessitates the formulation of diagnostic and treatment plans through MDTs. Initially, it is essential to predict MVI for resectable liver cancer patients, preferring surgical resection for high-risk patients and ensuring anatomic liver resection or a resection margin of ≥1 cm whenever possible. Institutions equipped to do so may consider intraoperative radiation therapy for patients with narrow margins. For those at low risk of MVI, liver cancers ≤3 cm in diameter may be treated with resection or PRFA/PMCT, while surgical resection is preferred for liver cancers >3 cm. Subsequently, for postoperative pathological MVI-positive and hepatitis B-related patients, routine antiviral therapy postsurgery is recommended, along with at least one postoperative adjuvant treatment based on the patient’s condition, including T+A, sintilimab, TACE, radiation therapy, HAIC, lenvatinib, or sorafenib. Finally, treatment plans for recurrent HCC patients with MVI need to be determined after MDT discussion.

Foresight

In recent years, significant progress has been made in the diagnosis, prediction, and treatment of HCC patients with MVI, but several issues urgently need to be addressed, such as the following: (I) the accuracy of predicting MVI still needs improvement. The prediction method recommended in this consensus also has many limitations and many important factors are not included in the nanogram, such as preoperative anticancer treatment, etc. A previous study confirmed that a single clinical indicator is insufficient to accurately predict MVI. Introducing new variables, such as omics data, may enhance the predictive accuracy. (II) The pathological examination of MVI needs further refinement. The most used pathological sampling method, the “7-point method”, has a significantly lower detection rate for MVI than IDS. However, the widespread adoption of IDS is challenging; thus, balancing sampling, detection rates, and application needs further exploration (258). (III) Despite the large number of HCC patients with MVI in China, the current consensus recommendations are relatively limited. Future efforts should utilize China’s vast case resources, integrated with the latest treatment advances such as targeted immunotherapy and dual immunotherapy, to conduct more randomized controlled trials to establish more effective treatment methods and plans for HCC patients with MVI. (IV) Further research into the molecular mechanisms of MVI in liver cancer is needed to identify new targets and therapies for treatment.

Acknowledgments

We thank Prof. Law Wan Yee, Zheng Shusen, Chen Xiaoping, Dong Jiahong, Fan Jia, Teng Gaojun, Cai Jianqiang, Chen Minshan, Dai Chaoliu, Fang Chihua, Ji Zhili, Wen Tianfu, Xia Jinglin, Ying Mingang, Zhang Zhiwei, Zhang Xuewen, Bi Xinyu, Che Xu, Chen Gang, Cheng Zhangjun, Cong Wenming, Shan Yunfeng, Dong Hui, Fan Ruifang, Feng Shuang, Guo Rongping, Guo Weixing, Hua Xiangdong, Jia Weidong, Jia Ningyang, Jin Bin, Li Bin, Li Guangxin, Li Jing, Li Jiangtao, Li Qiu, Li Wei, Liu Enyu, Liu Fubao, Liu Wensong, Ma Yong, Meng Yan, Peng Wei, Qian Yeben, Shi Jie, Sui Chengjun, Sun Beicheng, Sun Zhiwei, Tan Guang, Tang Yufu, Wan Chun, Wang Kang, Wang Lu, Wei Wenjing, Wei Xubiao, Xiang Bangde, Xu Liu, Yan Maolin, Zhai Jian, Zeng Zhaochong, Zhang Jialin, Zhang Xiuping, Zhang Yu, Zhang Zhensheng, Zhang Aibin, Zhao Haitao, Zhou Cuncai, Zhou Hongkun, Zhou Huabang, Zhou Jian, Zhou Jinxue, Zhou Weiping for their help for this article.

Footnote

Peer Review File: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-359/prf

Funding: This work was supported by the National Key R&D Program of China (No. 2022YFC2503700); and Shanghai Top Priority Research Center Construction Project (No. 2023ZZ02005).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-24-359/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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References

1. Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin 2023;73:17-48. [Crossref] [PubMed]
2. Huang G, Lau WY, Wang ZG, et al. Antiviral therapy improves postoperative survival in patients with hepatocellular carcinoma: a randomized controlled trial. Ann Surg 2015;261:56-66. [Crossref] [PubMed]
3. Tabrizian P, Jibara G, Shrager B, et al. Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis. Ann Surg 2015;261:947-55. [Crossref] [PubMed]
4. Mazzaferro V, Llovet JM, Miceli R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol 2009;10:35-43. [Crossref] [PubMed]
5. Xu X, Lu D, Ling Q, et al. Liver transplantation for hepatocellular carcinoma beyond the Milan criteria. Gut 2016;65:1035-41. [Crossref] [PubMed]
6. Xu SL, Zhang YC, Wang GY, et al. Survival analysis of sirolimus-based immunosuppression in liver transplantation in patients with hepatocellular carcinoma. Clin Res Hepatol Gastroenterol 2016;40:674-81. [Crossref] [PubMed]
7. Duvoux C, Roudot-Thoraval F, Decaens T, et al. Liver transplantation for hepatocellular carcinoma: a model including α-fetoprotein improves the performance of Milan criteria. Gastroenterology 2012;143:986-94.e3; quiz e14-5. [Crossref] [PubMed]
8. Howick J, Chalmers I, Glasziou P, et al. OCEBM Levels of Evidence Working Group ‘The Oxford 2011 Levels of Evidence’: Oxford Centre for Evidence-Based Medicine. Oxford; 2011.
9. U.S. Preventive Services Task Force. Grade Definitions and Suggestions for Practice. 2012; Available online: http://www.uspreventiveservicestaskforce.org/Page/Name/grade-definitions
10. Cong WM, Bu H, Chen J, et al. Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update. World J Gastroenterol 2016;22:9279-87. [Crossref] [PubMed]
11. Altendorf-Hofmann A, Scheele J. Indication and surgical outcome in hepatocellular carcinoma with infiltration of blood vessels, bile ducts and lymph nodes. Kongressbd Dtsch Ges Chir Kongr 2002;119:635-41.
12. Cucchetti A, Piscaglia F, Grigioni AD, et al. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol 2010;52:880-8. [Crossref] [PubMed]
13. Hung HH, Lei HJ, Chau GY, et al. Milan criteria, multi-nodularity, and microvascular invasion predict the recurrence patterns of hepatocellular carcinoma after resection. J Gastrointest Surg 2013;17:702-11. [Crossref] [PubMed]
14. Jun L, Zhenlin Y, Renyan G, et al. Independent factors and predictive score for extrahepatic metastasis of hepatocellular carcinoma following curative hepatectomy. Oncologist 2012;17:963-9. [Crossref] [PubMed]
15. Huang C, Zhu XD, Ji Y, et al. Microvascular invasion has limited clinical values in hepatocellular carcinoma patients at Barcelona Clinic Liver Cancer (BCLC) stages 0 or B. BMC Cancer 2017;17:58. [Crossref] [PubMed]
16. Imai K, Yamashita YI, Yusa T, et al. Microvascular Invasion in Small-sized Hepatocellular Carcinoma: Significance for Outcomes Following Hepatectomy and Radiofrequency Ablation. Anticancer Res 2018;38:1053-60. [Crossref] [PubMed]
17. Li ZL, Yu JJ, Guo JW, et al. Liver resection is justified for multinodular hepatocellular carcinoma in selected patients with cirrhosis: A multicenter analysis of 1,066 patients. Eur J Surg Oncol 2019;45:800-7. [Crossref] [PubMed]
18. Shindoh J, Kobayashi Y, Kawamura Y, et al. Microvascular Invasion and a Size Cutoff Value of 2 cm Predict Long-Term Oncological Outcome in Multiple Hepatocellular Carcinoma: Reappraisal of the American Joint Committee on Cancer Staging System and Validation Using the Surveillance, Epidemiology, and End-Results Database. Liver Cancer 2020;9:156-66. [Crossref] [PubMed]
19. Zhang XP, Zhou TF, Wang ZH, et al. Association of Preoperative Hypercoagulability with Poor Prognosis in Hepatocellular Carcinoma Patients with Microvascular Invasion After Liver Resection: A Multicenter Study. Ann Surg Oncol 2019;26:4117-25. [Crossref] [PubMed]
20. Sheng X, Ji Y, Ren GP, et al. A standardized pathological proposal for evaluating microvascular invasion of hepatocellular carcinoma: a multicenter study by LCPGC. Hepatol Int 2020;14:1034-47. [Crossref] [PubMed]
21. Liu H, Yang Y, Chen C, et al. Reclassification of tumor size for solitary HBV-related hepatocellular carcinoma by minimum p value method: a large retrospective study. World J Surg Oncol 2020;18:185. [Crossref] [PubMed]
22. Lee S, Kim SH, Lee JE, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol 2017;67:526-34. [Crossref] [PubMed]
23. Esnaola NF, Lauwers GY, Mirza NQ, et al. Predictors of microvascular invasion in patients with hepatocellular carcinoma who are candidates for orthotopic liver transplantation. J Gastrointest Surg 2002;6:224-32; discussion 232. [Crossref] [PubMed]
24. Kim MJ, Lee M, Choi JY, et al. Imaging features of small hepatocellular carcinomas with microvascular invasion on gadoxetic acid-enhanced MR imaging. Eur J Radiol 2012;81:2507-12. [Crossref] [PubMed]
25. Shindoh J, Andreou A, Aloia TA, et al. Microvascular invasion does not predict long-term survival in hepatocellular carcinoma up to 2 cm: reappraisal of the staging system for solitary tumors. Ann Surg Oncol 2013;20:1223-9. [Crossref] [PubMed]
26. Yamashita YI, Imai K, Yusa T, et al. Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: Predictors and optimal treatments. Ann Gastroenterol Surg 2018;2:197-203. [Crossref] [PubMed]
27. Bhattacharjya S, Bhattacharjya T, Quaglia A, et al. Liver transplantation in cirrhotic patients with small hepatocellular carcinoma: an analysis of pre-operative imaging, explant histology and prognostic histologic indicators. Dig Surg 2004;21:152-9; discussion 159-60. [Crossref] [PubMed]
28. Zhou JM, Zhou CY, Chen XP, et al. Anatomic resection improved the long-term outcome of hepatocellular carcinoma patients with microvascular invasion: A prospective cohort study. World J Gastrointest Oncol 2021;13:2190-202. [Crossref] [PubMed]
29. Löhe F, Angele MK, Rentsch M, et al. Multifocal manifestation does not affect vascular invasion of hepatocellular carcinoma: implications for patient selection in liver transplantation. Clin Transplant 2007;21:696-701. [Crossref] [PubMed]
30. Nagano Y, Shimada H, Takeda K, et al. Predictive factors of microvascular invasion in patients with hepatocellular carcinoma larger than 5 cm. World J Surg 2008;32:2218-22. [Crossref] [PubMed]
31. Kim JM, Kwon CH, Joh JW, et al. The effect of alkaline phosphatase and intrahepatic metastases in large hepatocellular carcinoma. World J Surg Oncol 2013;11:40. [Crossref] [PubMed]
32. Lim C, Mise Y, Sakamoto Y, et al. Above 5 cm, size does not matter anymore in patients with hepatocellular carcinoma. World J Surg 2014;38:2910-8. [Crossref] [PubMed]
33. Kluger MD, Salceda JA, Laurent A, et al. Liver resection for hepatocellular carcinoma in 313 Western patients: tumor biology and underlying liver rather than tumor size drive prognosis. J Hepatol 2015;62:1131-40. [Crossref] [PubMed]
34. Kim PT, Jang JH, Atenafu EG, et al. Outcomes after hepatic resection and subsequent multimodal treatment of recurrence for multifocal hepatocellular carcinoma. Br J Surg 2013;100:1516-22. [Crossref] [PubMed]
35. Wang BW, Mok KT, Liu SI, et al. Is hepatectomy beneficial in the treatment of multinodular hepatocellular carcinoma? J Formos Med Assoc 2008;107:616-26. [Crossref] [PubMed]
36. Goh BK, Chow PK, Teo JY, et al. Number of nodules, Child-Pugh status, margin positivity, and microvascular invasion, but not tumor size, are prognostic factors of survival after liver resection for multifocal hepatocellular carcinoma. J Gastrointest Surg 2014;18:1477-85. [Crossref] [PubMed]
37. Wang H, Qian YW, Wu MC, et al. Liver Resection Is Justified in Patients with BCLC Intermediate Stage Hepatocellular Carcinoma without Microvascular Invasion. J Gastrointest Surg 2020;24:2737-47. [Crossref] [PubMed]
38. Unek T, Karademir S, Arslan NC, et al. Comparison of Milan and UCSF criteria for liver transplantation to treat hepatocellular carcinoma. World J Gastroenterol 2011;17:4206-12. [Crossref] [PubMed]
39. Zheng J, Chou JF, Gönen M, et al. Prediction of Hepatocellular Carcinoma Recurrence Beyond Milan Criteria After Resection: Validation of a Clinical Risk Score in an International Cohort. Ann Surg 2017;266:693-701. [Crossref] [PubMed]
40. Hwang YJ, Bae JS, Lee Y, et al. Classification of microvascular invasion of hepatocellular carcinoma: correlation with prognosis and magnetic resonance imaging. Clin Mol Hepatol 2023;29:733-46. [Crossref] [PubMed]
41. Sumie S, Nakashima O, Okuda K, et al. The significance of classifying microvascular invasion in patients with hepatocellular carcinoma. Ann Surg Oncol 2014;21:1002-9. [Crossref] [PubMed]
42. Feng LH, Dong H, Lau WY, et al. Novel microvascular invasion-based prognostic nomograms to predict survival outcomes in patients after R0 resection for hepatocellular carcinoma. J Cancer Res Clin Oncol 2017;143:293-303. [Crossref] [PubMed]
43. Zhao H, Chen C, Fu X, et al. Prognostic value of a novel risk classification of microvascular invasion in patients with hepatocellular carcinoma after resection. Oncotarget 2017;8:5474-86. [Crossref] [PubMed]
44. Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology 2009;137:850-5. [Crossref] [PubMed]
45. Chen L, Chen S, Zhou Q, et al. Microvascular Invasion Status and Its Survival Impact in Hepatocellular Carcinoma Depend on Tissue Sampling Protocol. Ann Surg Oncol 2021;28:6747-57. [Crossref] [PubMed]
46. Iguchi T, Shirabe K, Aishima S, et al. New Pathologic Stratification of Microvascular Invasion in Hepatocellular Carcinoma: Predicting Prognosis After Living-donor Liver Transplantation. Transplantation 2015;99:1236-42. [Crossref] [PubMed]
47. Parfitt JR, Marotta P, Alghamdi M, et al. Recurrent hepatocellular carcinoma after transplantation: use of a pathological score on explanted livers to predict recurrence. Liver Transpl 2007;13:543-51. [Crossref] [PubMed]
48. Xu XF, Diao YK, Zeng YY, et al. Association of severity in the grading of microvascular invasion with long-term oncological prognosis after liver resection for early-stage hepatocellular carcinoma: a multicenter retrospective cohort study from a hepatitis B virus-endemic area. Int J Surg 2023;109:841-9. [Crossref] [PubMed]
49. Sun L, Sun Z, Wang C, et al. PCformer: an MVI recognition method via classification of the MVI boundary according to histopathological images of liver cancer. J Opt Soc Am A Opt Image Sci Vis 2022;39:1673-81. [Crossref] [PubMed]
50. Zhang XP, Wang K, Wei XB, et al. An Eastern Hepatobiliary Surgery Hospital Microvascular Invasion Scoring System in Predicting Prognosis of Patients with Hepatocellular Carcinoma and Microvascular Invasion After R0 Liver Resection: A Large-Scale, Multicenter Study. Oncologist 2019;24:e1476-88. [Crossref] [PubMed]
51. Wang K, Xiang YJ, Yu HM, et al. A novel classification in predicting prognosis and guiding postoperative management after R0 liver resection for patients with hepatocellular carcinoma and microvascular invasion. Eur J Surg Oncol 2022;48:1348-55. [Crossref] [PubMed]
52. Schlichtemeier SM, Pang TC, Williams NE, et al. A pre-operative clinical model to predict microvascular invasion and long-term outcome after resection of hepatocellular cancer: The Australian experience. Eur J Surg Oncol 2016;42:1576-83. [Crossref] [PubMed]
53. Shen J, Wen T, Chen W, et al. Model predicting the microvascular invasion and satellite lesions of hepatocellular carcinoma after hepatectomy. ANZ J Surg 2018;88:E761-6. [Crossref] [PubMed]
54. Li P, Huang W, Wang F, et al. Nomograms based on inflammatory biomarkers for predicting tumor grade and micro-vascular invasion in stage I/II hepatocellular carcinoma. Biosci Rep 2018;38:BSR20180464. [Crossref] [PubMed]
55. Ma X, Wei J, Gu D, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 2019;29:3595-605. [Crossref] [PubMed]
56. Zhang C, Zhao R, Chen F, et al. Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis. Transl Oncol 2021;14:100875. [Crossref] [PubMed]
57. Wang Y, Luo S, Jin G, et al. Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text]F-FDG PET/CT. BMC Med Imaging 2022;22:70.
58. Zhang S, Duan C, Zhou X, et al. Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA. Front Oncol 2022;12:1034519. [Crossref] [PubMed]
59. Sheng H, Mao M, Huang K, et al. A Clinical Tool to Predict the Microvascular Invasion Risk in Patients with Hepatocellular Carcinoma. Technol Cancer Res Treat 2023;22:15330338231182526. [Crossref] [PubMed]
60. Wang L, Ke Q, Lin K, et al. Not All Hepatocellular Carcinoma Patients with Microvascular Invasion After R0 Resection Could Be Benefited from Prophylactic Transarterial Chemoembolization: A Propensity Score Matching Study. Cancer Manag Res 2020;12:3815-25. [Crossref] [PubMed]
61. Reginelli A, Vacca G, Segreto T, et al. Can microvascular invasion in hepatocellular carcinoma be predicted by diagnostic imaging? A critical review. Future Oncol 2018;14:2985-94. [Crossref] [PubMed]
62. Nitta H, Allard MA, Sebagh M, et al. Prognostic Value and Prediction of Extratumoral Microvascular Invasion for Hepatocellular Carcinoma. Ann Surg Oncol 2019;26:2568-76. [Crossref] [PubMed]
63. Yan Y, Zhou Q, Zhang M, et al. Integrated Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma Patients. Ann Surg Oncol 2020;27:1361-71. [Crossref] [PubMed]
64. Ryu T, Takami Y, Wada Y, et al. A Clinical Scoring System for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma Within the Milan Criteria. J Gastrointest Surg 2019;23:779-87. [Crossref] [PubMed]
65. Zhang K, Xie SS, Li WC, et al. Prediction of microvascular invasion in HCC by a scoring model combining Gd-EOB-DTPA MRI and biochemical indicators. Eur Radiol 2022;32:4186-97. [Crossref] [PubMed]
66. Liu W, Zhang L, Xin Z, et al. A Promising Preoperative Prediction Model for Microvascular Invasion in Hepatocellular Carcinoma Based on an Extreme Gradient Boosting Algorithm. Front Oncol 2022;12:852736. [Crossref] [PubMed]
67. Wang X, Fu Y, Zhu C, et al. New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China. Front Surg 2022;9:1046713. [Crossref] [PubMed]
68. Herrero A, Boivineau L, Cassese G, et al. Progression of AFP SCORE is a Preoperative Predictive Factor of Microvascular Invasion in Selected Patients Meeting Liver Transplantation Criteria for Hepatocellular Carcinoma. Transpl Int 2022;35:10412. [Crossref] [PubMed]
69. Jiang H, Wei J, Fu F, et al. Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI. Liver Int 2022;42:1158-72. [Crossref] [PubMed]
70. Xu W, Wang Y, Yang Z, et al. New Insights Into a Classification-Based Microvascular Invasion Prediction Model in Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2022;12:796311. [Crossref] [PubMed]
71. Bai S, Yang P, Wei Y, et al. Development and validation of prognostic dynamic nomograms for hepatitis B Virus-related hepatocellular carcinoma with microvascular invasion after curative resection. Front Oncol 2023;13:1166327. [Crossref] [PubMed]
72. Zhu W, Qing X, Yan F, et al. Can the Contrast-Enhanced Ultrasound Washout Rate Be Used to Predict Microvascular Invasion in Hepatocellular Carcinoma? Ultrasound Med Biol 2017;43:1571-80. [Crossref] [PubMed]
73. Grąt M, Stypułkowski J, Patkowski W, et al. Limitations of predicting microvascular invasion in patients with hepatocellular cancer prior to liver transplantation. Sci Rep 2017;7:39881. [Crossref] [PubMed]
74. Wang L, Jin YX, Ji YZ, et al. Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma. World J Gastroenterol 2020;26:1647-59. [Crossref] [PubMed]
75. Lee JC, Hung HC, Wang YC, et al. Risk Score Model for Microvascular Invasion in Hepatocellular Carcinoma: The Role of Tumor Burden and Alpha-Fetoprotein. Cancers (Basel) 2021;13:4403. [Crossref] [PubMed]
76. Kang WH, Hwang S, Kaibori M, et al. Validation of quantitative prognostic prediction using ADV score for resection of hepatocellular carcinoma: A Korea-Japan collaborative study with 9200 patients. J Hepatobiliary Pancreat Sci 2023;30:993-1005. [Crossref] [PubMed]
77. Forlemu AN, Nana Sede Mbakop R, Bandaru P, et al. Liver Segment Disposition of Hepatocellular Carcinoma Predicts Microvascular Invasion. Int J Hepatol 2023;2023:5727701. [Crossref] [PubMed]
78. Zhang K, Zhang L, Li WC, et al. Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study. Eur Radiol 2023;33:8936-47. [Crossref] [PubMed]
79. Nitta H, Allard MA, Sebagh M, et al. Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatic resection or liver transplantation. Surgery 2019;165:1168-75. [Crossref] [PubMed]
80. Yang Y, Fan W, Gu T, et al. Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma. Front Oncol 2021;11:756216. [Crossref] [PubMed]
81. Zhou Q, Zhou C, Yin Y, et al. Development and validation of a nomogram combining hematological and imaging features for preoperative prediction of microvascular invasion in hepatocellular carcinoma patients. Ann Transl Med 2021;9:402. [Crossref] [PubMed]
82. Arsenii N, Piardi T, Diébold MD, et al. Impact of Visceral Obesity on Microvascular Invasion in Hepatocellular Carcinoma. Cancer Invest 2016;34:271-8. [Crossref] [PubMed]
83. Xu L, Zhang M, Zheng X, et al. The circular RNA ciRS-7 (Cdr1as) acts as a risk factor of hepatic microvascular invasion in hepatocellular carcinoma. J Cancer Res Clin Oncol 2017;143:17-27. [Crossref] [PubMed]
84. Yang C, Wang H, Tang Y, et al. ADC similarity predicts microvascular invasion of bifocal hepatocellular carcinoma. Abdom Radiol (NY) 2018;43:2295-302. [Crossref] [PubMed]
85. Wang K, Xiang Y, Yan J, et al. A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy. Hepatol Int 2022;16:1188-98. [Crossref] [PubMed]
86. Chang Y, Guo T, Zhu B, et al. A novel nomogram for predicting microvascular invasion in hepatocellular carcinoma. Ann Hepatol 2023;28:101136. [Crossref] [PubMed]
87. Zhang XP, Chai ZT, Feng JK, et al. Association of type 2 diabetes mellitus with incidences of microvascular invasion and survival outcomes in hepatitis B virus-related hepatocellular carcinoma after liver resection: A multicenter study. Eur J Surg Oncol 2022;48:142-9. [Crossref] [PubMed]
88. You Z, Chen LP, Ye H. Predictors of microvascular invasion in patients with solitary small hepatitis B related hepatocellular carcinoma. Pak J Med Sci 2014;30:331-4. [Crossref] [PubMed]
89. Zhang K, Tao C, Siqin T, et al. Establishment, validation and evaluation of predictive model for early relapse after R0 resection in hepatocellular carcinoma patients with microvascular invasion. J Transl Med 2021;19:293. [Crossref] [PubMed]
90. Lei Z, Li J, Wu D, et al. Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatitis B Virus-Related Hepatocellular Carcinoma Within the Milan Criteria. JAMA Surg 2016;151:356-63. [Crossref] [PubMed]
91. Qu C, Huang X, Liu K, et al. Effect of hepatitis B virus DNA replication level and anti-HBV therapy on microvascular invasion of hepatocellular carcinoma. Infect Agent Cancer 2019;14:2. [Crossref] [PubMed]
92. Hu H, Qi S, Zeng S, et al. Importance of Microvascular Invasion Risk and Tumor Size on Recurrence and Survival of Hepatocellular Carcinoma After Anatomical Resection and Non-anatomical Resection. Front Oncol 2021;11:621622. [Crossref] [PubMed]
93. Chong H, Zhou P, Yang C, et al. An excellent nomogram predicts microvascular invasion that cannot independently stratify outcomes of small hepatocellular carcinoma. Ann Transl Med 2021;9:757. [Crossref] [PubMed]
94. Zhang W, Liu Z, Chen J, et al. A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria. Transl Oncol 2021;14:101200. [Crossref] [PubMed]
95. Li H, Li T, Hu J, et al. A nomogram to predict microvascular invasion in early hepatocellular carcinoma. J Cancer Res Ther 2021;17:652-7. [Crossref] [PubMed]
96. Mao S, Yu X, Yang Y, et al. Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma. Sci Rep 2021;11:13999. [Crossref] [PubMed]
97. Liu B, Zeng Q, Huang J, et al. IVIM using convolutional neural networks predicts microvascular invasion in HCC. Eur Radiol 2022;32:7185-95. [Crossref] [PubMed]
98. Kiriyama S, Uchiyama K, Ueno M, et al. Triple positive tumor markers for hepatocellular carcinoma are useful predictors of poor survival. Ann Surg 2011;254:984-91. [Crossref] [PubMed]
99. Hirokawa F, Hayashi M, Miyamoto Y, et al. Outcomes and predictors of microvascular invasion of solitary hepatocellular carcinoma. Hepatol Res 2014;44:846-53. [Crossref] [PubMed]
100. Kobayashi T, Aikata H, Honda F, et al. Preoperative Fluorine 18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for Prediction of Microvascular Invasion in Small Hepatocellular Carcinoma. J Comput Assist Tomogr 2016;40:524-30. [Crossref] [PubMed]
101. Imura S, Teraoku H, Yoshikawa M, et al. Potential predictive factors for microvascular invasion in hepatocellular carcinoma classified within the Milan criteria. Int J Clin Oncol 2018;23:98-103. [Crossref] [PubMed]
102. Chen Z, Zuo X, Zhang Y, et al. Derivation and validation of machine learning models for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma. Ann Transl Med 2023;11:249. [Crossref] [PubMed]
103. Wang S, Zheng W, Zhang Z, et al. Microvascular invasion risk scores affect the estimation of early recurrence after resection in patients with hepatocellular carcinoma: a retrospective study. BMC Med Imaging 2022;22:204. [Crossref] [PubMed]
104. Wei J, Jiang H, Zeng M, et al. Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study. Cancers (Basel) 2021;13:2368. [Crossref] [PubMed]
105. Yoh T, Seo S, Ogiso S, et al. Quantitative assessment of microvascular invasion in hepatocellular carcinoma using preoperative serological and imaging markers. HPB (Oxford) 2021;23:1039-45. [Crossref] [PubMed]
106. Wang H, Lu Y, Liu R, et al. A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma. Front Oncol 2021;11:745085. [Crossref] [PubMed]
107. Xia F, Zhang Q, Ndhlovu E, et al. A nomogram for preoperative prediction of microvascular invasion in ruptured hepatocellular carcinoma. Eur J Gastroenterol Hepatol 2023;35:591-9. [Crossref] [PubMed]
108. Yue Q, Zhou Z, Zhang X, et al. Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma. BMC Gastroenterol 2022;22:544. [Crossref] [PubMed]
109. Chong HH, Yang L, Sheng RF, et al. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur Radiol 2021;31:4824-38. [Crossref] [PubMed]
110. Tang M, Zhou Q, Huang M, et al. Nomogram development and validation to predict hepatocellular carcinoma tumor behavior by preoperative gadoxetic acid-enhanced MRI. Eur Radiol 2021;31:8615-27. [Crossref] [PubMed]
111. Okamura Y, Sugiura T, Ito T, et al. Anatomical resection is useful for the treatment of primary solitary hepatocellular carcinoma with predicted microscopic vessel invasion and/or intrahepatic metastasis. Surg Today 2021;51:1429-39. [Crossref] [PubMed]
112. Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 2019;70:1133-44. [Crossref] [PubMed]
113. Zhao WC, Fan LF, Yang N, et al. Preoperative predictors of microvascular invasion in multinodular hepatocellular carcinoma. Eur J Surg Oncol 2013;39:858-64. [Crossref] [PubMed]
114. Fu SJ, Zhao Q, Ji F, et al. Elevated Preoperative Serum Gamma-glutamyltranspeptidase Predicts Poor Prognosis for Hepatocellular Carcinoma after Liver Transplantation. Sci Rep 2016;6:28835. [Crossref] [PubMed]
115. Zhao H, Hua Y, Lu Z, et al. Prognostic value and preoperative predictors of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm without macrovascular invasion. Oncotarget 2017;8:61203-14. [Crossref] [PubMed]
116. Carr BI, Ince V, Bag HG, et al. Microscopic vascular invasion by hepatocellular carcinoma in liver transplant patients. Clin Pract (Lond) 2020;17:1497-505.
117. Gu Y, Zheng F, Zhang Y, et al. Novel Nomogram Based on Inflammatory Markers for the Preoperative Prediction of Microvascular Invasion in Solitary Primary Hepatocellular Carcinoma. Cancer Manag Res 2022;14:895-907. [Crossref] [PubMed]
118. He M, Zhang P, Ma X, et al. Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma. Front Oncol 2020;10:574228. [Crossref] [PubMed]
119. Liu J, Zhu Q, Li Y, et al. Microvascular invasion and positive HB e antigen are associated with poorer survival after hepatectomy of early hepatocellular carcinoma: A retrospective cohort study. Clin Res Hepatol Gastroenterol 2018;42:330-8. [Crossref] [PubMed]
120. Guo X, Zhang W, Du J, et al. Acute-Phase Serum Amyloid A May Predict Microvascular Invasion and Early Tumor Recurrence in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma Undergoing Liver Resection. J Invest Surg 2022;35:1368-76. [Crossref] [PubMed]
121. Zhu YJ, Feng B, Wang S, et al. Model-based three-dimensional texture analysis of contrast-enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Oncol Lett 2019;18:720-32. [Crossref] [PubMed]
122. Zhou L, Wang SB, Chen SG, et al. Risk factors of recurrence and poor survival in curatively resected hepatocellular carcinoma with microvascular invasion. Adv Clin Exp Med 2020;29:887-92. [Crossref] [PubMed]
123. Chen S, Wang C, Gu Y, et al. Corrigendum: Prediction of Microvascular Invasion and Its M2 Classification in Hepatocellular Carcinoma Based on Nomogram Analyses. Front Oncol 2022;12:888008. [Crossref] [PubMed]
124. Yao J, Li K, Yang H, et al. Analysis of Sonazoid contrast-enhanced ultrasound for predicting the risk of microvascular invasion in hepatocellular carcinoma: a prospective multicenter study. Eur Radiol 2023;33:7066-76. [Crossref] [PubMed]
125. Wang H, Wu MC, Cong WM. Microvascular invasion predicts a poor prognosis of solitary hepatocellular carcinoma up to 2 cm based on propensity score matching analysis. Hepatol Res 2019;49:344-54. [Crossref] [PubMed]
126. Yanhan W, Lianfang L, Hao L, et al. Effect of Microvascular Invasion on the Prognosis in Hepatocellular Carcinoma and Analysis of Related Risk Factors: A Two-Center Study. Front Surg 2021;8:733343. [Crossref] [PubMed]
127. Portolani N, Baiocchi GL, Molfino S, et al. Microvascular infiltration has limited clinical value for treatment and prognosis in hepatocellular carcinoma. World J Surg 2014;38:1769-76. [Crossref] [PubMed]
128. Xu X, Sun S, Liu Q, et al. Preoperative application of systemic inflammatory biomarkers combined with MR imaging features in predicting microvascular invasion of hepatocellular carcinoma. Abdom Radiol (NY) 2022;47:1806-16. [Crossref] [PubMed]
129. Endo Y, Alaimo L, Lima HA, et al. A Novel Online Calculator to Predict Risk of Microvascular Invasion in the Preoperative Setting for Hepatocellular Carcinoma Patients Undergoing Curative-Intent Surgery. Ann Surg Oncol 2023;30:725-33. [Crossref] [PubMed]
130. Zhu Y, Xu D, Zhang Z, et al. A new laboratory-based algorithm to predict microvascular invasion and survival in patients with hepatocellular carcinoma. Int J Surg 2018;57:45-53. [Crossref] [PubMed]
131. Rungsakulkij N, Mingphruedhi S, Suragul W, et al. Platelet-to-Lymphocyte Ratio and Large Tumor Size Predict Microvascular Invasion after Resection for Hepatocellular Carcinoma. Asian Pac J Cancer Prev 2018;19:3435-41. [Crossref] [PubMed]
132. Li S, Zeng Q, Liang R, et al. Using Systemic Inflammatory Markers to Predict Microvascular Invasion Before Surgery in Patients With Hepatocellular Carcinoma. Front Surg 2022;9:833779. [Crossref] [PubMed]
133. Zhang H, Zhou Y, Li Y, et al. Predictive value of gamma-glutamyl transpeptidase to lymphocyte count ratio in hepatocellular carcinoma patients with microvascular invasion. BMC Cancer 2020;20:132. [Crossref] [PubMed]
134. Siegel AB, Wang S, Jacobson JS, et al. Obesity and microvascular invasion in hepatocellular carcinoma. Cancer Invest 2010;28:1063-9. [Crossref] [PubMed]
135. Siegel AB, Lim EA, Wang S, et al. Diabetes, body mass index, and outcomes in hepatocellular carcinoma patients undergoing liver transplantation. Transplantation 2012;94:539-43. [Crossref] [PubMed]
136. He YZ, He K, Huang RQ, et al. Preoperative evaluation and prediction of clinical scores for hepatocellular carcinoma microvascular invasion: a single-center retrospective analysis. Ann Hepatol 2020;19:654-61. [Crossref] [PubMed]
137. Zhang W, Liu L, Wang P, et al. Preoperative computed tomography and serum α-fetoprotein to predict microvascular invasion in hepatocellular carcinoma. Medicine (Baltimore) 2018;97:e11402. [Crossref] [PubMed]
138. Zhou J, Zhang Z, Zhou H, et al. Preoperative circulating tumor cells to predict microvascular invasion and dynamical detection indicate the prognosis of hepatocellular carcinoma. BMC Cancer 2020;20:1047. [Crossref] [PubMed]
139. Tang Y, Lu X, Liu L, et al. A Reliable and Repeatable Model for Predicting Microvascular Invasion in Patients With Hepatocellular Carcinoma. Acad Radiol 2023;30:1521-7. [Crossref] [PubMed]
140. Dong SY, Wang WT, Chen XS, et al. Microvascular invasion of small hepatocellular carcinoma can be preoperatively predicted by the 3D quantification of MRI. Eur Radiol 2022;32:4198-209. [Crossref] [PubMed]
141. Zhang J, Zeng F, Jiang S, et al. Preoperative prediction model of microvascular invasion in patients with hepatocellular carcinoma. HPB (Oxford) 2023;25:45-53. [Crossref] [PubMed]
142. Liu HF, Zhang YZ, Wang Q, et al. A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria. Transl Oncol 2023;27:101597. [Crossref] [PubMed]
143. Zhou L, Qu Y, Quan G, et al. Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging. Acad Radiol 2024;31:457-66. [Crossref] [PubMed]
144. Zheng X, Xu YJ, Huang J, et al. Predictive value of radiomics analysis of enhanced CT for three-tiered microvascular invasion grading in hepatocellular carcinoma. Med Phys 2023;50:6079-95. [Crossref] [PubMed]
145. Wu Y, Zhu M, Liu Y, et al. Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022;12:907076. [Crossref] [PubMed]
146. Gao S, Zhang Y, Sun W, et al. Assessment of an MR Elastography-Based Nomogram as a Potential Imaging Biomarker for Predicting Microvascular Invasion of Hepatocellular Carcinoma. J Magn Reson Imaging 2023;58:392-402. [Crossref] [PubMed]
147. Witjes CD, Willemssen FE, Verheij J, et al. Histological differentiation grade and microvascular invasion of hepatocellular carcinoma predicted by dynamic contrast-enhanced MRI. J Magn Reson Imaging 2012;36:641-7. [Crossref] [PubMed]
148. Zhang S, Huo L, Zhang J, et al. A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm). Front Oncol 2022;12:992301. [Crossref] [PubMed]
149. Zhao H, Hua Y, Dai T, et al. Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma. Eur J Radiol 2017;88:32-40. [Crossref] [PubMed]
150. Li L, Su Q, Yang H. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: a radiomic nomogram based on MRI. Clin Radiol 2022;77:e269-79. [Crossref] [PubMed]
151. Yang J, Dong X, Wang G, et al. Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma. Abdom Radiol (NY) 2023;48:554-66. [Crossref] [PubMed]
152. Takayasu K, Arii S, Sakamoto M, et al. Clinical implication of hypovascular hepatocellular carcinoma studied in 4,474 patients with solitary tumour equal or less than 3 cm. Liver Int 2013;33:762-70. [Crossref] [PubMed]
153. Kim JG, Jang KM, Min GS, et al. Questionable correlation of the apparent diffusion coefficient with the histological grade and microvascular invasion in small hepatocellular carcinoma. Clin Radiol 2019;74:406.e19-27. [Crossref] [PubMed]
154. Rao C, Wang X, Li M, et al. Value of T1 mapping on gadoxetic acid-enhanced MRI for microvascular invasion of hepatocellular carcinoma: a retrospective study. BMC Med Imaging 2020;20:43. [Crossref] [PubMed]
155. Chen J, Guo Y, Guo Y, et al. Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study. Magn Reson Imaging 2023;95:110-7. [Crossref] [PubMed]
156. Wang F, Yan CY, Wang CH, et al. The Roles of Diffusion Kurtosis Imaging and Intravoxel Incoherent Motion Diffusion-Weighted Imaging Parameters in Preoperative Evaluation of Pathological Grades and Microvascular Invasion in Hepatocellular Carcinoma. Front Oncol 2022;12:884854. [Crossref] [PubMed]
157. Yang H, Han P, Huang M, et al. The role of gadoxetic acid-enhanced MRI features for predicting microvascular invasion in patients with hepatocellular carcinoma. Abdom Radiol (NY) 2022;47:948-56. [Crossref] [PubMed]
158. Li H, Zhang J, Zheng Z, et al. Preoperative histogram analysis of intravoxel incoherent motion (IVIM) for predicting microvascular invasion in patients with single hepatocellular carcinoma. Eur J Radiol 2018;105:65-71. [Crossref] [PubMed]
159. Wei Y, Huang Z, Tang H, et al. IVIM improves preoperative assessment of microvascular invasion in HCC. Eur Radiol 2019;29:5403-14. [Crossref] [PubMed]
160. Li M, Yin Z, Hu B, et al. MR Elastography-Based Shear Strain Mapping for Assessment of Microvascular Invasion in Hepatocellular Carcinoma. Eur Radiol 2022;32:5024-32. [Crossref] [PubMed]
161. Zhang L, Li M, Zhu J, et al. The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma. Eur Radiol 2023;33:4103-14. [Crossref] [PubMed]
162. Zhang Y, Yang C, Sheng R, et al. Preoperatively Identify the Microvascular Invasion of Hepatocellular Carcinoma with the Restricted Spectrum Imaging. Acad Radiol 2023;30:S30-9. [Crossref] [PubMed]
163. Gao SX, Liao R, Wang HQ, et al. A Nomogram Predicting Microvascular Invasion Risk in BCLC 0/A Hepatocellular Carcinoma after Curative Resection. Biomed Res Int 2019;2019:9264137. [Crossref] [PubMed]
164. Matsumoto N, Ogawa M, Kaneko M, et al. Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion. J Ultrasound Med 2021;40:689-98. [Crossref] [PubMed]
165. Xu C, Jiang D, Tan B, et al. Preoperative diagnosis and prediction of microvascular invasion in hepatocellularcarcinoma by ultrasound elastography. BMC Med Imaging 2022;22:88. [Crossref] [PubMed]
166. Cheung TT, Chan SC, Ho CL, et al. Can positron emission tomography with the dual tracers [11 C]acetate and [18 F]fludeoxyglucose predict microvascular invasion in hepatocellular carcinoma?. Liver Transpl 2011;17:1218-25.
167. Ahn SY, Lee JM, Joo I, et al. Prediction of microvascular invasion of hepatocellular carcinoma using gadoxetic acid-enhanced MR and (18)F-FDG PET/CT. Abdom Imaging 2015;40:843-51. [Crossref] [PubMed]
168. Hyun SH, Eo JS, Song BI, et al. Preoperative prediction of microvascular invasion of hepatocellular carcinoma using 18F-FDG PET/CT: a multicenter retrospective cohort study. Eur J Nucl Med Mol Imaging 2018;45:720-6. [Crossref] [PubMed]
169. Lim C, Salloum C, Chalaye J, et al. 18F-FDG PET/CT predicts microvascular invasion and early recurrence after liver resection for hepatocellular carcinoma: A prospective observational study. HPB (Oxford) 2019;21:739-47. [Crossref] [PubMed]
170. Kim K, Kim SJ. Diagnostic test accuracies of F-18 FDG PET/CT for prediction of microvascular invasion of hepatocellular carcinoma: A meta-analysis. Clin Imaging 2021;79:251-8. [Crossref] [PubMed]
171. Ye Z, Zhang J, Wu N, et al. PET-guided attention for prediction of microvascular invasion in preoperative hepatocellular carcinoma on PET/CT. Ann Nucl Med 2023;37:238-45. [Crossref] [PubMed]
172. Chou CT, Chen RC, Lee CW, et al. Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging. Br J Radiol 2012;85:778-83. [Crossref] [PubMed]
173. Deng Y, Yang D, Xu H, et al. Diagnostic performance of imaging features in the HBP of gadoxetate disodium-enhanced MRI for microvascular invasion in hepatocellular carcinoma: a meta-analysis. Acta Radiol 2022;63:1303-14. [Crossref] [PubMed]
174. Deng Y, Li J, Xu H, et al. Diagnostic Accuracy of the Apparent Diffusion Coefficient for Microvascular Invasion in Hepatocellular Carcinoma: A Meta-analysis. J Clin Transl Hepatol 2022;10:642-50. [Crossref] [PubMed]
175. Zhong X, Long H, Su L, et al. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2022;47:2071-88. [Crossref] [PubMed]
176. Zhou HY, Cheng JM, Chen TW, et al. CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Clinics (Sao Paulo) 2023;78:100264. [Crossref] [PubMed]
177. Liang G, Yu W, Liu S, et al. The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis. Front Oncol 2022;12:960944. [Crossref] [PubMed]
178. Xiao Q, Zhu W, Tang H, et al. Ultrasound radiomics in the prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Heliyon 2023;9:e16997. [Crossref] [PubMed]
179. Dong Y, Wang QM, Li Q, et al. Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals. Front Oncol 2019;9:1203. [Crossref] [PubMed]
180. Li Z, Wang Y, Zhu Y, et al. Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma. Front Oncol 2023;13:1195110. [Crossref] [PubMed]
181. You H, Wang J, Ma R, et al. Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism. Bioengineering (Basel) 2023;10:948. [Crossref] [PubMed]
182. Cao L, Wang Q, Hong J, et al. MVI-TR: A Transformer-Based Deep Learning Model with Contrast-Enhanced CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancers (Basel) 2023;15:1538. [Crossref] [PubMed]
183. Wang F, Chen Q, Chen Y, et al. A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma. Eur J Surg Oncol 2023;49:156-64. [Crossref] [PubMed]
184. Chen YD, Zhang L, Zhou ZP, et al. Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma. World J Gastroenterol 2022;28:4399-416. [Crossref] [PubMed]
185. Qin X, Zhu J, Tu Z, et al. Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma. Acad Radiol 2023;30:S73-80. [Crossref] [PubMed]
186. Sun BY, Gu PY, Guan RY, et al. Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma. World J Surg Oncol 2022;20:189. [Crossref] [PubMed]
187. Wang L, Wu M, Li R, et al. MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancers (Basel) 2022;14:2956. [Crossref] [PubMed]
188. Chu T, Zhao C, Zhang J, et al. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022;29:6774-83. [Crossref] [PubMed]
189. Lee S, Kang TW, Song KD, et al. Effect of Microvascular Invasion Risk on Early Recurrence of Hepatocellular Carcinoma After Surgery and Radiofrequency Ablation. Ann Surg 2021;273:564-71. [Crossref] [PubMed]
190. Sun SW, Liu QP, Xu X, et al. Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI. J Magn Reson Imaging 2020;52:433-47. [Crossref] [PubMed]
191. Bai S, Yang P, Xie Z, et al. Preoperative Estimated Risk of Microvascular Invasion is Associated with Prognostic Differences Following Liver Resection Versus Radiofrequency Ablation for Early Hepatitis B Virus-Related Hepatocellular Carcinoma. Ann Surg Oncol 2021;28:8174-85. [Crossref] [PubMed]
192. Yang P, Teng F, Bai S, et al. Liver resection versus liver transplantation for hepatocellular carcinoma within the Milan criteria based on estimated microvascular invasion risks. Gastroenterol Rep (Oxf) 2023;11:goad035. [Crossref] [PubMed]
193. Chan SC, Fan ST, Chok KS, et al. Survival advantage of primary liver transplantation for hepatocellular carcinoma within the up-to-7 criteria with microvascular invasion. Hepatol Int 2012;6:646-56. [Crossref] [PubMed]
194. Vitale A, Huo TL, Cucchetti A, et al. Survival Benefit of Liver Transplantation Versus Resection for Hepatocellular Carcinoma: Impact of MELD Score. Ann Surg Oncol 2015;22:1901-7. [Crossref] [PubMed]
195. Jeong J, Park JG, Seo KI, et al. Microvascular invasion may be the determining factor in selecting TACE as the initial treatment in patients with hepatocellular carcinoma. Medicine (Baltimore) 2021;100:e26584. [Crossref] [PubMed]
196. Yang Y, Dang Z, Lu P, et al. Impact of pathological response after preoperative transcatheter arterial chemoembolization (TACE) on incidences of microvascular invasion and early tumor recurrence in hepatocellular carcinoma: a multicenter propensity score matching analysis. Hepatobiliary Surg Nutr 2022;11:386-99. [Crossref] [PubMed]
197. Yang Y, Lin K, Liu L, et al. Impact of preoperative TACE on incidences of microvascular invasion and long-term post-hepatectomy survival in hepatocellular carcinoma patients: A propensity score matching analysis. Cancer Med 2021;10:2100-11. [Crossref] [PubMed]
198. Kim JM, Kwon CH, Joh JW, et al. Effectiveness of locoregional therapy before living donor liver transplantation in patients with hepatocellular carcinoma who meet the Milan criteria. Transplant Proc 2012;44:403-8. [Crossref] [PubMed]
199. Wei X, Jiang Y, Feng S, et al. Neoadjuvant intensity modulated radiotherapy for a single and small (≤5 cm) hepatitis B virus-related hepatocellular carcinoma predicted to have high risks of microvascular invasion: a randomized clinical trial. Int J Surg 2023;109:3052-60. [Crossref] [PubMed]
200. Sun Z, Li Z, Shi XL, et al. Anatomic versus non-anatomic resection of hepatocellular carcinoma with microvascular invasion: A systematic review and meta-analysis. Asian J Surg 2021;44:1143-50. [Crossref] [PubMed]
201. Zheng J, Wang N, Yuan J, et al. The appropriate method of hepatectomy for hepatocellular carcinoma within University of California San Francisco (UCSF) criteria through neural network analysis. HPB (Oxford) 2023;25:497-506. [Crossref] [PubMed]
202. Hirose Y, Sakata J, Takizawa K, et al. Impact of anatomic resection on long-term survival in patients with hepatocellular carcinoma with T1-T2 disease or microscopic vascular invasion. Surg Oncol 2023;49:101951. [Crossref] [PubMed]
203. Meng XP, Tang TY, Ding ZM, et al. Preoperative Microvascular Invasion Prediction to Assist in Surgical Plan for Single Hepatocellular Carcinoma: Better Together with Radiomics. Ann Surg Oncol 2022;29:2960-70. [Crossref] [PubMed]
204. Shi C, Zhao Q, Liao B, et al. Anatomic resection and wide resection margin play an important role in hepatectomy for hepatocellular carcinoma with peritumoural micrometastasis. ANZ J Surg 2019;89:E482-6. [Crossref] [PubMed]
205. Zhong XP, Zhang YF, Mei J, et al. Anatomical versus Non-anatomical Resection for Hepatocellular Carcinoma with Microscope Vascular Invasion: A Propensity Score Matching Analysis. J Cancer 2019;10:3950-7. [Crossref] [PubMed]
206. Lin S, Ye F, Rong W, et al. Nomogram to Assist in Surgical Plan for Hepatocellular Carcinoma: a Prediction Model for Microvascular Invasion. J Gastrointest Surg 2019;23:2372-82. [Crossref] [PubMed]
207. Cucchetti A, Qiao GL, Cescon M, et al. Anatomic versus nonanatomic resection in cirrhotic patients with early hepatocellular carcinoma. Surgery 2014;155:512-21. [Crossref] [PubMed]
208. Fan LF, Zhao WC, Yang N, et al. Alpha-fetoprotein: the predictor of microvascular invasion in solitary small hepatocellular carcinoma and criterion for anatomic or non-anatomic hepatic resection. Hepatogastroenterology 2013;60:825-36. [Crossref] [PubMed]
209. Chen ZH, Zhang XP, Feng JK, et al. Actual long-term survival in hepatocellular carcinoma patients with microvascular invasion: a multicenter study from China. Hepatol Int 2021;15:642-50. [Crossref] [PubMed]
210. Yang SY, Yan ML, Duan YF, et al. Perioperative and long-term survival outcomes of laparoscopic versus laparotomic hepatectomy for BCLC stages 0-A hepatocellular carcinoma patients associated with or without microvascular invasion: a multicenter, propensity score matching analysis. Hepatol Int 2022;16:892-905. [Crossref] [PubMed]
211. Wang H, Yu H, Qian YW, et al. Impact of Surgical Margin on the Prognosis of Early Hepatocellular Carcinoma (≤5 cm): A Propensity Score Matching Analysis. Front Med (Lausanne) 2020;7:139. [Crossref] [PubMed]
212. Han J, Li ZL, Xing H, et al. The impact of resection margin and microvascular invasion on long-term prognosis after curative resection of hepatocellular carcinoma: a multi-institutional study. HPB (Oxford) 2019;21:962-71. [Crossref] [PubMed]
213. Yang P, Si A, Yang J, et al. A wide-margin liver resection improves long-term outcomes for patients with HBV-related hepatocellular carcinoma with microvascular invasion. Surgery 2019;165:721-30. [Crossref] [PubMed]
214. Liu M, Wang L, Zhu H, et al. A Preoperative Measurement of Serum MicroRNA-125b May Predict the Presence of Microvascular Invasion in Hepatocellular Carcinomas Patients. Transl Oncol 2016;9:167-72. [Crossref] [PubMed]
215. Liu J, Zhuang G, Bai S, et al. The Comparison of Surgical Margins and Type of Hepatic Resection for Hepatocellular Carcinoma With Microvascular Invasion. Oncologist 2023;28:e1043-51. [Crossref] [PubMed]
216. Zhang XP, Xu S, Lin ZY, et al. Significance of anatomical resection and resection margin status in patients with HBV-related hepatocellular carcinoma and microvascular invasion: a multicenter propensity score-matched study. Int J Surg 2023;109:679-88. [Crossref] [PubMed]
217. Shi F, Zhou Z, Huang X, et al. Is anatomical resection necessary for early hepatocellular carcinoma? A single institution retrospective experience. Future Oncol 2019;15:2041-51. [Crossref] [PubMed]
218. Shin S, Kim TS, Lee JW, et al. Is the anatomical resection necessary for single hepatocellular carcinoma smaller than 3 cm?: single-center experience of liver resection for a small HCC. Ann Hepatobiliary Pancreat Surg 2018;22:326-34. [Crossref] [PubMed]
219. Wang L, Liu Y, Rong W, et al. The role of intraoperative electron radiotherapy in centrally located hepatocellular carcinomas treated with narrow-margin (<1 cm) hepatectomy: a prospective, phase 2 study. Hepatobiliary Surg Nutr 2022;11:515-29. [Crossref] [PubMed]
220. Wang Z, Duan Y, Zhang J, et al. Preoperative antiviral therapy and microvascular invasion in hepatitis B virus-related hepatocellular carcinoma: A meta-analysis. Eur J Pharmacol 2020;883:173382. [Crossref] [PubMed]
221. Liu K, Duan J, Liu H, et al. Precancer antiviral treatment reduces microvascular invasion of early-stage Hepatitis B-related hepatocellular carcinoma. Sci Rep 2019;9:2220. [Crossref] [PubMed]
222. Tang Y, Zhang J, Chen G, et al. Efficacy of Adjuvant Transarterial Chemoembolization Combined Antiviral Therapy for HBV-Related HCC with MVI after Hepatic Resection: A Multicenter Study. Asian Pac J Cancer Prev 2022;23:2695-703. [Crossref] [PubMed]
223. Kong J, Liang X, Zhang J, et al. Antiviral Therapy Improves Survival in Hepatocellular Carcinoma with Microvascular Invasion: A Propensity Score Analysis. Dig Dis Sci 2022;67:4250-7. [Crossref] [PubMed]
224. Li L, Li B, Zhang M. HBV DNA levels impact the prognosis of hepatocellular carcinoma patients with microvascular invasion. Medicine (Baltimore) 2019;98:e16308. [Crossref] [PubMed]
225. Li Z, Lei Z, Xia Y, et al. Association of Preoperative Antiviral Treatment With Incidences of Microvascular Invasion and Early Tumor Recurrence in Hepatitis B Virus-Related Hepatocellular Carcinoma. JAMA Surg 2018;153:e182721. [Crossref] [PubMed]
226. Sun JJ, Wang K, Zhang CZ, et al. Postoperative Adjuvant Transcatheter Arterial Chemoembolization After R0 Hepatectomy Improves Outcomes of Patients Who have Hepatocellular Carcinoma with Microvascular Invasion. Ann Surg Oncol 2016;23:1344-51. [Crossref] [PubMed]
227. Wei W, Jian PE, Li SH, et al. Adjuvant transcatheter arterial chemoembolization after curative resection for hepatocellular carcinoma patients with solitary tumor and microvascular invasion: a randomized clinical trial of efficacy and safety. Cancer Commun (Lond) 2018;38:61. [Crossref] [PubMed]
228. Mo A, Lin B, Chen D. Efficacy of sequential TACE on primary hepatocellular carcinoma with microvascular invasion after radical resection: a systematic review and meta-analysis. World J Surg Oncol 2023;21:277. [Crossref] [PubMed]
229. Liu ZH, Chai ZT, Feng JK, et al. A reasonable identification of the early recurrence time based on microvascular invasion for hepatocellular carcinoma after R0 resection: A multicenter retrospective study. Cancer Med 2023;12:10294-302. [Crossref] [PubMed]
230. Chen ZH, Zhang XP, Zhou TF, et al. Adjuvant transarterial chemoembolization improves survival outcomes in hepatocellular carcinoma with microvascular invasion: A systematic review and meta-analysis. Eur J Surg Oncol 2019;45:2188-96. [Crossref] [PubMed]
231. Ye JZ, Chen JZ, Li ZH, et al. Efficacy of postoperative adjuvant transcatheter arterial chemoembolization in hepatocellular carcinoma patients with microvascular invasion. World J Gastroenterol 2017;23:7415-24. [Crossref] [PubMed]
232. Luo L, Shan R, Cui L, et al. Postoperative adjuvant transarterial chemoembolisation improves survival of hepatocellular carcinoma patients with microvascular invasion: A multicenter retrospective cohort. United European Gastroenterol J 2023;11:228-41. [Crossref] [PubMed]
233. Gao Z, Du G, Pang Y, et al. Adjuvant transarterial chemoembolization after radical resection contributed to the outcomes of hepatocellular carcinoma patients with high-risk factors. Medicine (Baltimore) 2017;96:e7426. [Crossref] [PubMed]
234. Shi C, Li Y, Geng L, et al. Adjuvant stereotactic body radiotherapy after marginal resection for hepatocellular carcinoma with microvascular invasion: A randomised controlled trial. Eur J Cancer 2022;166:176-84. [Crossref] [PubMed]
235. Wang L, Wang W, Rong W, et al. Postoperative adjuvant treatment strategy for hepatocellular carcinoma with microvascular invasion: a non-randomized interventional clinical study. BMC Cancer 2020;20:614. [Crossref] [PubMed]
236. Wang L, Wang W, Yao X, et al. Postoperative adjuvant radiotherapy is associated with improved survival in hepatocellular carcinoma with microvascular invasion. Oncotarget 2017;8:79971-81. [Crossref] [PubMed]
237. Wang L, Chen B, Li Z, et al. Optimal postoperative adjuvant treatment strategy for HBV-related hepatocellular carcinoma with microvascular invasion: a propensity score analysis. Onco Targets Ther 2019;12:1237-47. [Crossref] [PubMed]
238. Li SH, Mei J, Cheng Y, et al. Postoperative Adjuvant Hepatic Arterial Infusion Chemotherapy With FOLFOX in Hepatocellular Carcinoma With Microvascular Invasion: A Multicenter, Phase III, Randomized Study. J Clin Oncol 2023;41:1898-908. [Crossref] [PubMed]
239. Hu L, Zheng Y, Lin J, et al. Does adjuvant hepatic artery infusion chemotherapy improve patient outcomes for hepatocellular carcinoma following liver resection? A meta-analysis. World J Surg Oncol 2023;21:121. [Crossref] [PubMed]
240. Pinyol R, Montal R, Bassaganyas L, et al. Molecular predictors of prevention of recurrence in HCC with sorafenib as adjuvant treatment and prognostic factors in the phase 3 STORM trial. Gut 2019;68:1065-75. [Crossref] [PubMed]
241. Huang Y, Zhang Z, Zhou Y, et al. Should we apply sorafenib in hepatocellular carcinoma patients with microvascular invasion after curative hepatectomy? Onco Targets Ther 2019;12:541-8. [Crossref] [PubMed]
242. Zhang XP, Chai ZT, Gao YZ, et al. Postoperative adjuvant sorafenib improves survival outcomes in hepatocellular carcinoma patients with microvascular invasion after R0 liver resection: a propensity score matching analysis. HPB (Oxford) 2019;21:1687-96. [Crossref] [PubMed]
243. Gu W, Tong Z. Sorafenib in the treatment of patients with hepatocellular carcinoma (HCC) and microvascular infiltration: a systematic review and meta-analysis. J Int Med Res 2020;48:300060520946872. [Crossref] [PubMed]
244. Bai S, Hu L, Liu J, et al. Prognostic Nomograms Combined Adjuvant Lenvatinib for Hepatitis B Virus-related Hepatocellular Carcinoma With Microvascular Invasion After Radical Resection. Front Oncol 2022;12:919824. [Crossref] [PubMed]
245. Dai MG, Liu SY, Lu WF, et al. Survival Benefits From Adjuvant Lenvatinib for Patients With Hepatocellular Carcinoma and Microvascular Invasion After Curative Hepatectomy. Clin Med Insights Oncol 2023;17:11795549231180351. [Crossref] [PubMed]
246. Qin S, Chen M, Cheng AL, et al. Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): a randomised, open-label, multicentre, phase 3 trial. Lancet 2023;402:1835-47. [Crossref] [PubMed]
247. Wang K, Xiang YJ, Yu HM, et al. Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med 2024;30:708-15. [Crossref] [PubMed]
248. Yang Y, Sun J, Wu M, et al. Chinese Expert Consensus on Immunotherapy for Hepatocellular Carcinoma (2021 Edition). Liver Cancer 2022;11:511-26. [Crossref] [PubMed]
249. Feng W, Cao W, Cui C, et al. Efficacy of Lipid Nanoparticle-Loaded Sorafenib Combined with Hepatic Artery Chemoembolization in the Treatment of Primary Hepatocellular Carcinoma Complicated with Microvascular Invasion. Dis Markers 2022;2022:4996471. [Crossref] [PubMed]
250. Wu J, Sun H, Han Z, et al. A single center experience: post-transplantation adjuvant chemotherapy impacts the prognosis of hepatocellular carcinoma patients. Chin Med J (Engl) 2014;127:430-4.
251. Oh N, Rhu J, Kim JM, et al. Improved recurrence-free survival in patients with HCC with post-transplant plasma exchange. Liver Transpl 2023;29:804-12. [Crossref] [PubMed]
252. Xiao H, Chen ZB, Jin HL, et al. Treatment selection of recurrent hepatocellular carcinoma with microvascular invasion at the initial hepatectomy. Am J Transl Res 2019;11:1864-75.
253. Wen T, Jin C, Facciorusso A, et al. Multidisciplinary management of recurrent and metastatic hepatocellular carcinoma after resection: an international expert consensus. Hepatobiliary Surg Nutr 2018;7:353-71. [Crossref] [PubMed]
254. Peng Z, Wu X, Li J, et al. The role of neoadjuvant conventional transarterial chemoembolization with radiofrequency ablation in the treatment of recurrent hepatocellular carcinoma after initial hepatectomy with microvascular invasion. Int J Hyperthermia 2022;39:688-96. [Crossref] [PubMed]
255. Sun X, Yang Z, Mei J, et al. The guiding value of microvascular invasion for treating early recurrent small hepatocellular carcinoma. Int J Hyperthermia 2021;38:931-8. [Crossref] [PubMed]
256. Wei MC, Zhang YJ, Chen MS, et al. Adjuvant Sorafenib Following Radiofrequency Ablation for Early-Stage Recurrent Hepatocellular Carcinoma With Microvascular Invasion at the Initial Hepatectomy. Front Oncol 2022;12:868429. [Crossref] [PubMed]
257. Peng Z, Chen S, Xiao H, et al. Microvascular Invasion as a Predictor of Response to Treatment with Sorafenib and Transarterial Chemoembolization for Recurrent Intermediate-Stage Hepatocellular Carcinoma. Radiology 2019;292:237-47. [Crossref] [PubMed]
258. Sun JX, Yang Z, Wu JY, et al. A new scoring system for predicting the outcome of hepatocellular carcinoma patients without microvascular invasion-a large-scale multicentre study. HPB (Oxford) 2024;26:741-52. [Crossref] [PubMed]