Radiomics to Predict Microvascular Invasion in Hepatocellular Carcinoma: A Promising Biomarker for Tumor Recurrence

Imaging Prognostic Biomarkers in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide with its incidence on the rise globally. This paper provides a comprehensive review of prognostic imaging markers in HCC, emphasizing their role in risk stratification and clinical decision-making. We explore quantitative and qualitative criteria derived from imaging studies, such as computed tomography (CT) and magnetic resonance imaging (MRI), which can offer valuable insights into the biological behavior of the tumor. While many of these markers are not yet widely integrated into current clinical guidelines, they represent a promising future direction for approaching this highly heterogeneous cancer. However, standardization and validation of these markers remain important challenges. We conclude by emphasizing the importance of ongoing research to enhance clinical practices and improve outcomes for patients with HCC.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

A Grading System of Microvascular Invasion for Patients with Hepatocellular Carcinoma Undergoing Liver Resection with Curative Intent: A Multicenter Study

Background

Microvascular invasion (MVI) is closely correlated with poor clinical outcomes in patients with hepatocellular carcinoma (HCC). A grading system of MVI is needed to assist in the management of HCC patient.

Methods

Multicenter data of HCC patients who underwent liver resection with curative intent was analyzed. This grading system was established by detected number and distance from tumor boundary of MVI. Survival outcomes were compared among patients in each group. This system was verified by time-receiver operating characteristic curve, time-area under the curve, calibration curve, and decision curve analyses. Cox regression analysis was performed to study the associated factors of prognosis. Logistic analysis was used to study the predictive factors of MVI.

Results

All patients were classified into 4 groups: M0: no MVI; M1: 1~5 proximal MVIs (≤1 cm from tumor boundary); M2a: >5 proximal MVIs (≤1 cm from tumor boundary); M2b: ≥1 distal MVIs (>1 cm from tumor boundary). The recurrence-free survival (RFS), overall survival (OS), and early RFS rates among all the individual groups were significantly different. Based on the number of proximal MVI (0~5 vs >5), patients in the M2b group were further divided into two subgroups which also showed different prognosis. Multiple methods showed this grading system to be significantly better than the MVI two-tiered system in prognostic evaluation. Four multivariate models for RFS, OS, early RFS, late RFS, and a predictive model of MVI were then established and were shown to satisfactorily evaluate prognosis and have a great discriminatory power, respectively.

Conclusion

This MVI grading system could precisely evaluate prognosis of HCC patients after liver resection with curative intent and it could be employed in routine pathological reports. The severity of MVI from both adjacent and distant from tumor boundary should be stated. A hypothesis about two occurrence modes of distal MVI was proposed.