A MRI-based radiomics model for preoperative prediction of five-year survival status in hepatocellular carcinoma.

Abstract

e15596 Background: Recurrence is the major cause of mortality in resected hepatocellular carcinoma (HCC) patients. However, without a standard approach to evaluate prognosis, it is difficult to select potential candidates for additional therapy. We aim to develop and evaluate a magnetic resonance imaging (MRI)-based radiomics model to predict 5-year survival status of HCC patients in the preoperative setting. Methods: A total of 201 HCC patients who were followed up for at least 5 years (unless death occurred) after curative hepatectomy were enrolled in this retrospective multicenter study. 3144 radiomics features were extracted from four conventional sequences of preoperative MRI (T1WI, T2WI, DWI and dynamic contrast-enhanced MRI). The random forest method was used for feature selection and radiomics signature building. 5-fold cross validation was used for robust estimation. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. The model performance was evaluated by its discrimination and calibration. Results: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on survival status at 5 years from surgery. The 30 most survival-related radiomics features were selected to develop the radiomics signature. The preoperative alpha-fetoprotein level was integrated into the model as an independent clinical risk factor in multivariable logistic regression analysis (OR = 3.764; 95% CI 1.997-7.096). The radiomics model demonstrated good calibration and satisfactory discrimination, with the mean area under the curve of 0.9340 (95% CI 0.9222-0.9458) in training set and 0.7383 (95% CI 0.6914-0.7852) in validation set. Conclusions: The MRI-based radiomics model represents a valid method to predict 5-year survival status in HCC patients in the preoperative setting, and may be used to guide neoadjuvant or adjuvant treatment decisions in high-risk patients.