MRI-Based Radiomics Predicts FAP Expression and Immunosuppressive Tumor Microenvironment in Hepatocellular Carcinoma

Purpose

To develop a Magnetic resonance imaging (MRI) -based radiomics-clinical model for noninvasive prediction of fibroblast activation protein (FAP) expression in hepatocellular carcinoma (HCC), and to explore its association with tumor microenvironment (TME) characteristics and potential immunotherapy response. Patients and methods: This retrospective study included 104 patients with HCC from the Fourth Hospital of Hebei Medical University, 31 external MRI cases from The Cancer Imaging Archive (TCIA), and 363 transcriptomic profiles from The Cancer Genome Atlas (TCGA). Immunohistochemistry (IHC) assessed FAP expression. Tumor regions were manually segmented, and radiomics features were extracted using PyRadiomics. Multiple machine learning classifiers were used to construct and validate models for predicting FAP expression. Logistic regression was selected for its balanced performance between the training and validation cohorts, with the smallest area under the curve (AUC) difference among the five machine-learning algorithms. A radiomics-clinical combined model was further developed by integrating the radscore with clinical indicators. Radiogenomic analysis explored radscore-related transcriptomic features and immune infiltration characteristics.