Non-invasive Radiomics Model for Preoperative Prediction of Dual-phenotype Hepatocellular Carcinoma Based on CT Data

BACKGROUND. Dual-phenotype hepatocellular carcinoma (DPHCC), a highly aggressive subtype, is difficult to diagnose preoperatively based on morphological characteristics. OBJECTIVE. We aimed to develop a radiomics model based on computed tomography (CT) data for non-invasive preoperative identification of DPHCC. METHODS. CT images and clinical data of 258 patients from Institution I were included in this study as a training set. Among them, 119 patients were diagnosed with DPHCC, and the rest 139 patients were treated as a control group. Radiomics features were extracted from regions of interest (ROI), and the features were selected by recursive feature elimination with cross-validation (RFECV). The logistic regression (LR) and random forest (RF) algorithm were used to develop four models to differentiate DPHCC and non-DPHCC, including the radiomics model, the clinical model, the radiologic model and the fused model. In addition, the effectiveness of the prediction models was evaluated by various indexes. CT images and clinical data of 58 patients from Institution II were included as an independent validation set. The models were evaluated on the independent validation set to assess the robustness of these models. RESULTS. Among these models, the radiomics model shows a balance of effectiveness and robustness. For the radiomics model, the mean area under the curve (AUC) of five-fold cross-validation on the training set reached 0.753, while the AUC on the independent validation set was 0.734. A radiomics signature containing 35 radiomics features was established for non-invasive prediction of dual-phenotype hepatocellular carcinoma. The fused model exhibits the best performance, with a training set AUC of 0.885 and an independent validation set AUC of 0.763. CONCLUSION. CT radiomics features of the tumor correlate significantly with the expression status of the bile duct phenotype in HCC. The radiomics feature, AFP level, ill-defined margin, pseudo-capsule and intra-tumoral necrosis are the essential features of the prediction model, potentially influencing the development of individualized treatment strategies for HCC. CLINICAL IMPACT. The radiomics model based on computed tomography (CT) data contribute to non-invasive preoperative identification of DPHCC.