Integrating Magnetic Resonance Imaging-Based Deep Learning and Radiomics to Predict Treatment Response in Hepatocellular Carcinoma Receiving Combined Systemic and Locoregional Therapies: A Multicenter Study.
Authors
Affiliations (12)
Affiliations (12)
- Department of Hepatobiliary Surgery and Liver Transplantation, Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China.
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
- Department of Hepatic Surgery (II), Eastern Hepatobiliary Surgery Hospital, Naval Medical University (Second Military Medical University), Shanghai, PR China.
- Department of Hepatic Oncology, Liver Cancer Institute and Zhongshan Hospital, Fudan University, Shanghai, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
- Department of Hepatobiliary, National Clinical Research Center of Cancer, Oncology Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Insitute and Hospital, Tianjin, China.
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, China.
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China.
- Department of Hepatobiliary Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China.
Abstract
Although combining systemic and locoregional therapies has improved outcomes for hepatocellular carcinoma (HCC), identifying appropriate candidates remains challenging because no reliable tool predicts response. This retrospective multicenter study enrolled 334 HCC patients who received first-line locoregional-systemic therapy from nine centers in China. Following automated tumor segmentation using nnU-Net on pretreatment contrast-enhanced magnetic resonance imaging, predefined radiomics features were computationally extracted, and deep learning features were obtained via a ResNet convolutional neural network. Three types of predictive models for tumor response (radiomics, deep learning, and combined) were developed and validated on the training cohort (<i>n</i> = 213), internal validation cohort (IVC, <i>n</i> = 53), and external validation cohort (EVC, <i>n</i> = 68). Performance was assessed using area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI). Associations of model with progression-free survival (PFS) and overall survival (OS) were evaluated. Among the three models (multi-sequence radiomics, multi-sequence deep learning, and combined), the combined model showed the best predictive performance in both the training (AUC: 0.997, 95% CI: 0.994-1.000) and validation cohorts (AUC of IVC 0.900, 95% CI: 0.806-0.994; AUC of EVC: 0.871, 95% CI: 0.790-0.951). The combined model demonstrated significantly improved predictive performance compared with the multi-sequence radiomic model (training NRI = 43.9%, <i>p</i> < 0.001; IVC NRI = 38.7%, <i>p</i> = 0.038; EVC NRI = 3.2%, <i>p</i> = 0.806) and multi-sequence deep learning model (training NRI = 80.8%, IVC NRI = 72.7%, EVC NRI = 50.4%; all <i>p</i> < 0.05). Specifically, the combined model was significantly associated with both PFS and OS. The combined radiomics-deep learning model outperformed both single-modality models in predicting objective response and stratifying survival outcomes in unresectable HCC patients receiving combined systemic and locoregional therapy.