Multicenter Integration of MR Radiomics, Deep Learning, and Clinical Indicators for Predicting Hepatocellular Carcinoma Recurrence After Thermal Ablation

Abstract

Yandan Wang,1 Yong Zhang,2 Jincheng Xiao,3 Xiang Geng,3 Lujun Han,4 Junpeng Luo5 1Department of Otorhinolaryngology, Huaihe Hospital of Henan University, Kaifeng, 475000, People’s Republic of China; 2Department of Immunotherapy, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450003, People’s Republic of China; 3Department of Minimally Invasive Intervention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450000, People’s Republic of China; 4Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Cancer for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510030, People’s Republic of China; 5Translational Medical Center of Huaihe Hospital, Henan University, Kaifeng, 475000, People’s Republic of ChinaCorrespondence: Junpeng Luo, Translational Medical Center of Huaihe Hospital, Henan University, Kaifeng, 475000, People’s Republic of China, Email [email protected]: To develop and validate an innovative predictive model that integrates multisequence magnetic resonance (MR) radiomics, deep learning features, and clinical indicators to accurately predict the recurrence of hepatocellular carcinoma (HCC) after thermal ablation.Methods: This retrospective multicenter cohort study enrolled patients who were diagnosed with HCC and treated via thermal ablation. We extracted radiomic features from multisequence 3T MR images, analyzed these images using a 3D convolutional neural network (3D CNN), and incorporated clinical data into the model. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.Results: The study included 535 patients from three hospitals, comprising 462 males and 43 females. The RDC model, which stands for the Radiomics-Deep Learning-Clinical data model, demonstrated high predictive accuracy, achieving AUCs of 0.794 in the training set, 0.777 in the validation set, and 0.787 in the test set. Statistical analysis confirmed the model’s robustness and the significant contribution of the integrated features to its predictive capabilities.Conclusion: The RDC model effectively predicts HCC recurrence after thermal ablation by synergistically combining advanced imaging analysis and clinical parameters. This study highlights the potential of such integrative approaches to enhance prognostic assessments in HCC patients and offers a promising tool for clinical decision-making.Keywords: hepatocellular carcinoma, thermal ablation, radiomics, deep learning, MRI, prognostic modeling

Keywords

• hepatocellular carcinoma
• thermal ablation
• radiomics
• deep learning
• mri
• prognostic modeling