Radiomics and clinicoradiological factors as a promising approach for predicting microvascular invasion in hepatitis B-related hepatocellular carcinoma

Abstract

Microvascular invasion (MVI) is a critical factor in hepatocellular carcinoma (HCC) prognosis, particularly in hepatitis B virus (HBV)-related cases. This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk (M2) status in HBV-related HCC using contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors. The study analyzed 270 patients, creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction, and 0.865 and 0.798 for M2 status prediction in training and validation datasets, respectively. These results are comparable to previous radiomics-based approaches, which reinforces the potential of this method in MVI prediction. The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging. However, limitations, such as retrospective design and manual segmentation, highlight areas for improvement. The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies. It also suggests future research directions, such as exploring mechanistic links between radiomics features and MVI, as well as integrating additional biomarkers or imaging modalities. Overall, this study contributes significantly to HCC management, paving the way for more accurate, personalized treatment approaches in the era of precision oncology.

Keywords: Hepatocellular carcinoma; Hepatitis-B; Microvascular invasion; Radiomics; Predicting factors

Core Tip: This editorial examines a recent study that predicts microvascular invasion (MVI) in hepatitis B-related hepatocellular carcinoma (HCC) using contrast-enhanced computed tomography (CT) radiomics and clinicoradiological factors. The study developed models that achieve high predictive accuracy for MVI and high-risk (M2) status. These findings align with previous radiomics-based approaches, reinforcing their potential in MVI prediction. The strengths of the study include its focus on hepatitis B virus-related HCC and the use of widely accessible CT imaging. However, limitations such as retrospective design highlight areas for improvement. This research contributes significantly to HCC management, paving the way for more accurate, personalized treatment approaches in precision oncology.