Prediction of PD-1 Expression and Outcomes of Combined Therapy in Hepatocellular Carcinoma: an MRI-Based Radiomics Approach.

This study aims to assess the value of clinical and MRI radiomics features in noninvasively predicting programmed cell death protein 1 (PD-1) expression level and the response to anti-PD-1 immunotherapy combined with transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC). A total of 107 HCC patients (85 in training set and 22 in validation set) with PD-1 positive (n = 41) and negative (n = 66) were enrolled. Radiomics features were extracted from T2-weighted, fat suppression T2-weighted, contrast-enhanced, and diffusion-weighted images. A clinical model was constructed based on independent clinical risk factors (p < 0.05), while a radiomics model was developed utilizing the optimal radiomics signature. A radiomics nomogram model for predicting PD-1 integrated the significant clinical and radiomics features. The performance of nomogram was evaluated with respect to its discrimination and clinical utility and compared with that of clinical and radiomics prediction models. The radiomics nomogram, combining the clinical factors with the Rad-score, had best prediction performance (area under the curve [AUC]: 0.95 in the training set; AUC: 0.86 in the validation set). Decision curve analysis (DCA) showed that the nomogram exhibited superior accuracy in clinical assessment compared to the other models. The predicted high-risk group of PD-1 had longer overall survival (OS) than the predicted low-risk group after receiving anti-PD-1 therapy combined with TACE. The MRI-based radiomics nomogram performed well for identifying high-risk PD-1 group for combination therapy and may inform personalized treatment decision-making strategies.