Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.
Authors
Affiliations (13)
Affiliations (13)
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumour and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery & Translation, Wenzhou, 325035, Zhejiang, China.
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, Zhejiang, China.
- The Second Clinical College, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. [email protected].
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, Zhejiang, China. [email protected].
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. [email protected].
- Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumour and Bioengineering, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. [email protected].
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery & Translation, Wenzhou, 325035, Zhejiang, China. [email protected].
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China. [email protected].
Abstract
Early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after curative hepatectomy correlates with dismal prognosis. We hypothesized that body composition radiomics reflecting systemic metabolic-immunologic status could enhance ER prediction. This multi-center study aimed to develop and validate integrated radiomics-clinical machine learning (RCML) models for postoperative ER risk stratification. In this retrospective study, 258 ICC patients (2011-2022) from three institutions who underwent curative resection were enrolled. Body composition features were extracted from preoperative contrast-enhanced CT (L3 level). After minimum redundancy maximum relevance(mRMR) feature selection, radiomics-based ML(RML) models were constructed. Integrated RCML models combined radiomic features with clinical variables. Six ML algorithms were employed and performance assessed by area under the receiver operating characteristic curve (AUC) with five-fold cross-validation, and external testing. ER occurred in 134 patients (52%). The optimal RML model achieved AUC 0.82 with 15 selected features, outperforming clinical-only models (mean AUC 0.72). The support vector machine (SVM) based RCML models demonstrated superior performance (training AUC 0.86; external validation AUC 0.84). The RCML model achieved balanced classification metrics (sensitivity 0.80, specificity 0.87, F1-score 0.82), indicating robust generalizability. Statistical differences between SVM-models were validated using DeLong's test. All best-performing models significantly stratified high/low-risk groups with divergent survival (log-rank P < 0.001). Integration of body composition radiomics and clinical factors in RCML models significantly improves ER prediction for resected ICC, enabling clinically actionable risk stratification. This approach leverages routinely acquired preoperative CT to quantify metabolic-immunologic derangements, providing opportunities for personalized surveillance protocols targeting high-risk patients.