Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis

Abstract

Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. Methods: We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. Results: Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76–0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72–0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76–0.84). Conclusions: Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.

Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma

“…Preoperative prediction of MVI in patients with HCC is of great significance for clinical treatment decisions. Recently, many studies have confirmed that radiomics could be an accurate and effective tool for MVI prediction in HCC patients [ 21 , 22 ], which holds promise for the non-invasive prediction and personalized treatment. However, previous radiomics studies have only focused on static radiomics and have ignored the change pattern of the static features over time.…”

Interventional Oncology: 2024 Update

“…55 Radiogenomics in hepatocellular carcinoma has shown promise in identifying gene patterns related to aggressive imaging features and in predicting microscopic vascular invasion on pathology. 56 In lung cancer, lung ablation and radiogenomics has correlated imaging characteristics with mutation status, aiding in predicting treatment response. 57,58 KRAS mutation status 57 in colorectal cancer has been linked to outcomes after radio-embolization, guiding the need for larger ablation margins.…”