Computed tomography-based deep learning and multi-instance learning for predicting microvascular invasion and prognosis in hepatocellular carcinoma.

Abstract

Background: Microvascular invasion (MVI) is an important prognostic factor in hepatocellular carcinoma (HCC), but its preoperative prediction remains challenging.

Aim: To develop and validate a 2.5-dimensional (2.5D) deep learning-based multi-instance learning (MIL) model (MIL signature) for predicting MVI in HCC, evaluate and compare its performance against the radiomics signature and clinical signature, and assess its prognostic predictive value in both surgical resection and transcatheter arterial chemoembolization (TACE) cohorts.

Methods: A retrospective cohort consisting of 192 patients with pathologically confirmed HCC was included, of whom 68 were MVI-positive and 124 were MVI-negative. The patients were randomly assigned to a training set (134 patients) and a validation set (58 patients) in a 7:3 ratio. An additional 45 HCC patients undergoing TACE treatment were included in the TACE validation cohort. A modeling strategy based on computed tomography arterial phase images was implemented, utilizing 2.5D deep learning in combination with a MIL framework for the prediction of MVI in HCC. Moreover, this method was compared with the radiomics signature and clinical signatures, and the predictive performance of the various models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA), with DeLong's test applied to compare the area under the curve (AUC) between models. Kaplan-Meier curves were utilized to analyze differences in recurrence-free survival (RFS) or progression-free survival (PFS) among different HCC treatment cohorts stratified by MIL signature risk.

Results: MIL signature demonstrated superior performance in the validation set (AUC = 0.877), significantly surpassing the radiomics signature (AUC = 0.727, P = 0.047) and clinical signature (AUC = 0.631, P = 0.004). DCA curves indicated that the MIL signature provided a greater clinical net benefit across the full spectrum of risk thresholds. In the prognostic analysis, high- and low-risk groups stratified by the MIL signature exhibited significant differences in RFS within the surgical resection cohort (training set P = 0.0058, validation set P = 0.031) and PFS within the TACE treatment cohort (P = 0.045).

Conclusion: MIL signature demonstrates more accurate MVI prediction in HCC, surpassing radiomics signature and clinical signature, and offers precise prognostic stratification, thereby providing new technical support for personalized HCC treatment strategies.