Artificial intelligence for multi-time-point arterial phase contrast-enhanced MRI profiling to predict prognosis after transarterial chemoembolization in hepatocellular carcinoma

作者：Lanlin Yao, Hamzah Adwan, Simon Bernatz, Hao Li, Thomas J. Vogl · 发表于：La radiologia medica · 年份：2025 · DOI：10.1007/s11547-025-02043-6 · 被引用次数：7 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

PURPOSE: Contrast-enhanced magnetic resonance imaging (CE-MRI) monitoring across multiple time points is critical for optimizing hepatocellular carcinoma (HCC) prognosis during transarterial chemoembolization (TACE) treatment. The aim of this retrospective study is to develop and validate an artificial intelligence (AI)-powered models utilizing multi-time-point arterial phase CE-MRI data for HCC prognosis stratification in TACE patients. MATERIAL AND METHODS: A total of 543 individual arterial phase CE-MRI scans from 181 HCC patients were retrospectively collected in this study. All patients underwent TACE and longitudinal arterial phase CE-MRI assessments at three time points: prior to treatment, and following the first and second TACE sessions. Among them, 110 patients received TACE monotherapy, while the remaining 71 patients underwent TACE in combination with microwave ablation (MWA). All images were subjected to standardized preprocessing procedures. We developed an end-to-end deep learning model, ProgSwin-UNETR, based on the Swin Transformer architecture, to perform four-class prognosis stratification directly from input imaging data. The model was trained using multi-time-point arterial phase CE-MRI data and evaluated via fourfold cross-validation. Classification performance was assessed using the area under the receiver operating characteristic curve (AUC). For comparative analysis, we benchmarked performance against traditional radiomics-based classifiers and the mRE...