Multiparametric MRI-Based Deep Learning and Radiomics for Predicting Progression-Free Survival Benefit in Patients with Hepatocellular Carcinoma Treated with Immunotherapy and Targeted Therapy Plus Transarterial Chemoembolization: A Bicentric Study

The non-invasive biomarkers for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) treated with immunotherapy and molecular targeted therapy combined with transarterial chemoembolization (IMT-MTT-TACE) are urgently needed to identify individuals who are likely to benefit from this treatment regimen. This study aims to develop a non-invasive imaging biomarker for predicting PFS in patients with HCC receiving IMT-MTT-TACE, leveraging the integration of deep learning, radiomics, and clinical factors. This study included 180 patients with HCC who were treated with IMT-MTT-TACE at two medical centers. Radiomic features were extracted from six sequences of multiparametric magnetic resonance imaging. Deep learning features were extracted based on the ResNet50 algorithm. A Cox regression combined model was developed by integrating significant clinical, radiomics, and deep learning features. Model performance was evaluated using the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) analysis area under the curve (AUC). The C-reactive protein and alpha-fetoprotein in immunotherapy (CRAFITY) score was identified as an independent predictor of PFS ( P < 0.05). In three cohorts, the C-index values for the deep learning model were 0.757, 0.751, and 0.744, respectively. The C-index values for the combined model were 0.803, 0.746, and 0.744, respectively. In the time-dependent ROC curve analysis predicting 1-year PFS, the AUC values for the combined model were 0.934 (95% confidence interval CI: 0.881–0.986), 0.842 (95% CI: 0.699–0.984), and 0.862 (95% CI: 0.725–0.998). The deep learning-based combined model demonstrated good predictive performance and exhibited strong robustness and generalizability. The integration of CRAFITY score, radiomics, and deep learning features contributed to predicting PFS for patients with HCC undergoing IMT-MTT-TACE. This combined model holds promise for enabling precise pretreatment risk stratification and optimizing monitoring protocols, thereby guiding prognosis assessment and individualized clinical treatment decisions.