Novel Unsupervised Machine Learning Model Using Multi-Parametric Radiomics for Prognostic Stratification of Bifocal Hepatocellular Carcinoma.

Purpose: To identify radiomics subtypes that reflect tumor heterogeneity in bifocal hepatocellular carcinoma (bHCC) using an unsupervised machine learning approach. Additionally, to develop a preoperative model and a postoperative fusion model aimed at predicting recurrence-free survival (RFS) and overall survival (OS) in bHCC patients following hepatectomy. Methods: This retrospective study included 182 bHCC patients (91 in the training set, 91 in the test set). To capture the overall tumor characteristics, radiomics features were extracted from both lesions across six MR sequences and integrated using a two-lesion fusion approach to represent each patient as a single analytical entity. The similarity network fusion approach was utilized to construct a patient similarity matrix based on multi-sequence radiomic features, aiming to identify distinct subgroups that capture patterns of tumor imaging heterogeneity through spectral clustering. Multivariable Cox regression analysis was conducted to develop prognostic models for RFS and OS. The preoperative radiomics image heterogeneity (RIH) model and postoperative model including pathological features were built to predict prognosis of bHCC patients after hepatectomy. Results: Unsupervised clustering analysis based on multi-parametric radiomics revealed two subtypes correlated with distinct clinical outcomes, where high-radiomics image heterogeneity (high-RIH) was associated with poorer RFS (Log-rank Conclusion: This study identified two different radiomics subtypes in bHCC which could reveal the heterogeneity of bHCC and predict clinical outcomes in post-hepatectomy bHCC patients.