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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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 <i>P</i> &lt; 0.001).</p> Conclusion <p>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.</p>

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Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Yuqian Gan,
  • Ziyan Chen,
  • Enguang Zou,
  • Changfeng Cheng,
  • Weiqi Guan,
  • Zefeng Shen,
  • Lushuang Wang,
  • Jian Lin,
  • Yurong Wang,
  • Xin Zhao,
  • Ziyi Zhang,
  • Yi Wang,
  • Lijun Wu,
  • Bin Zhou,
  • Xiao Liang,
  • Gang Chen

摘要

Purpose

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.

Methods

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.

Results

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).

Conclusion

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.