Abstract
PURPOSE: Predicting the pathological response after neoadjuvant conversion therapy for initially unresectable hepatocellular carcinoma (HCC) is essential for surgical decision-making and survival outcomes but remains a challenge. We aimed to develop a radiomics model to predict pathological responses. METHODS: We included 203 patients with HCC who underwent hepatectomy after neoadjuvant conversion therapy between 2015 and 2023 and separated them into a training set (100 patients from Center A) and a validation set (103 patients from Center B). Pathological complete response (pCR)-related radiomic features were extracted from the largest tumor layer in the arterial and portal vein phases of the CT. A synthetic minority oversampling technique (SMOTE) was used to balance the minority groups in the training set. The SMOTE radiomics model was constructed using a logistic regression model in the SMOTE training set and its performance was verified in the validation set. RESULTS: The AUC of the preoperative modified response evaluation criteria in solid tumors (mRECIST) assessment for pCR was 0.656 and 0.589 in the training and validation sets, respectively. The SMOTE radiomics model was established based on ten radiomic features and showed good pCR-predictive performance in the SMOTE training set (AUC, 0.889; accuracy, 87.7%) and the validation set (AUC: 0.843, accuracy: 86.4%). The RFS of the radiomics-predicted-pCR group was significantly better than that of the predicted-non-pCR group in the training cohort (P = 0.001, 2-year RFS: 69.5% and 30.1% respectively) and the validation cohort (P = 0.012, 2-year RFS: 65.9% and 38.0% respectively). CONCLUSION: The SMOTE radiomics model has great potential for predicting pathological response and evaluating RFS in patients with unresectable HCC after neoadjuvant conversion therapy.