Radiomics Model Based on Gadoxetic Acid Disodium-Enhanced MR Imaging to Predict Hepatocellular Carcinoma Recurrence After Curative Ablation

Abstract

Ling Zhang,1,* Peiqiang Cai,1,* Jingyu Hou,2 Ma Luo,1 Yonggang Li,3 Xinhua Jiang1 1Department of Radiology, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People’s Republic of China; 2Department of Liver Surgery, Sun Yat-Sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xinhua JiangDepartment of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People’s Republic of ChinaTel/Fax [phone]Email [email protected] LiDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: A practical prognostic prediction model is absent for hepatocellular carcinoma (HCC) patients after curative ablation. We aimed to develop a radiomics model based on gadoxetic acid disodium-enhanced magnetic resonance (MR) images to predict HCC recurrence after curative ablation.Methods: We retrospectively enrolled 132 patients with HCC who underwent curative ablation. Patients were randomly divided into the training (n = 92) and validation (n = 40) cohorts. Radiomic features were extracted from gadoxetic acid disodium-enhanced MR images of the liver before curative ablation, and various baseline clinical characteristics were collected. Cox regression and random survival forests were used to construct models that incorporated radiomic features and/or clinical characteristics. The predictive performance of the different models was compared using the concordance index (C-index) and decision curves analysis (DCA). A cutoff derived from the combined model was used for risk categorization, and recurrence-free survival (RFS) was compared between groups using the Kaplan-Meier survival curve analysis.Results: Twenty radiomic features and four clinical characteristics were identified and used for model construction. The radiomics model constructed by tumoral and peritumoral radiomic features had better predictive performance (C-index 0.698, 95% confidence interval [CI] 0.640– 0.755) compared with the clinical model (C-index 0.614, 95% CI 0.499– 0.695), while the combined model had the best predictive performance (C-index 0.706, 95% CI 0.638– 0.763). A better net benefit was observed with the combined model compared with the other two models according to the DCA. Distinct RFS distributions were observed when patients were categorized based on the cutoff derived from the combined model (Log rank test, p = 0.007).Conclusion: The radiomics model which combined radiomic features extracted from gadoxetic acid disodium-enhanced MR images with clinical characteristics could predict HCC recurrence after curative ablation.Keywords: hepatocellular carcinoma, recurrence, ablation, magnetic resonance imaging, radiomics

Keywords

• hepatocellular carcinoma
• recurrence
• ablation
• magnetic resonance imaging
• radiomics