Three-dimensional multimodal imaging for predicting early recurrence of hepatocellular carcinoma after surgical resection.
Authors
Affiliations (8)
Affiliations (8)
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili 556000, China.
- Department of Health Management, Guangdong Provincial Key Laboratory for Prevention and Control of Major Liver Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Department of Medical Oncology, the First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.
- General Department of Oncology, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang 330029, China.
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China.
- Department of Oncology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330000, China. Electronic address: [email protected].
- Department of Health Management, Guangdong Provincial Key Laboratory for Prevention and Control of Major Liver Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: [email protected].
- Department of Health Management, Guangdong Provincial Key Laboratory for Prevention and Control of Major Liver Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: [email protected].
Abstract
High tumor recurrence after surgery remains a significant challenge in managing hepatocellular carcinoma (HCC). We aimed to construct a multimodal model to forecast the early recurrence of HCC after surgical resection and explore the associated biological mechanisms. Overall, 519 patients with HCC were included from three medical centers. 433 patients from Nanfang Hospital were used as the training cohort, and 86 patients from the other two hospitals comprised validation cohort. Radiomics and deep learning (DL) models were developed using contrast-enhanced computed tomography images. Radiomics feature visualization and gradient-weighted class activation mapping were applied to improve interpretability. A multimodal model (MM-RDLM) was constructed by integrating radiomics and DL models. Associations between MM-RDLM and recurrence-free survival (RFS) and overall survival were analyzed. Gene set enrichment analysis (GSEA) and multiplex immunohistochemistry (mIHC) were used to investigate the biological mechanisms. Models based on hepatic arterial phase images exhibited the best predictive performance, with radiomics and DL models achieving areas under the curve (AUCs) of 0.770 (95 % confidence interval [CI]: 0.725-0.815) and 0.846 (95 % CI: 0.807-0.886), respectively, in the training cohort. MM-RDLM achieved an AUC of 0.955 (95 % CI: 0.937-0.972) in the training cohort and 0.930 (95 % CI: 0.876-0.984) in the validation cohort. MM-RDLM (high vs. low) was notably linked to RFS in the training (hazard ratio [HR] = 7.80 [5.74 - 10.61], P < 0.001) and validation (HR = 10.46 [4.96 - 22.68], P < 0.001) cohorts. GSEA revealed enrichment of the natural killer cell-mediated cytotoxicity pathway in the MM-RDLM low cohort. mIHC showed significantly higher percentages of CD3-, CD56-, and CD8-positive cells in the MM-RDLM low group. The MM-RDLM model demonstrated strong predictive performance for early postoperative recurrence of HCC. These findings contribute to identifying patients at high risk for early recurrence and provide insights into the potential underlying biological mechanisms.