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Rationale and objectives <p>Early recurrence after curative resection remains a major determinant of poor prognosis in intrahepatic cholangiocarcinoma (ICC). Existing multimodal prediction models often lack interpretability due to feature interference during fusion. This study aimed to develop and externally validate an interpretable multimodal machine-learning model using a novel Independent Feature Selection and Consistent Integration (IFSCI) framework, combined with SHAP-based explanation to enhance transparency.</p> Materials and methods <p>A total of 264 patients with mass-forming ICC who underwent radical resection were retrospectively enrolled from two centers. Clinical and CT-based radiomics features were independently selected within each modality using statistical testing and LASSO under the IFSCI design, ensuring modality-specific interpretability before consistent integration. Support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were used to construct clinical, radiomics, and combined models. Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis (DCA). SHAP values were applied to provide global and case-level interpretability.</p> Results <p>Six clinical and twenty radiomics features were retained. The MLP-based combined model demonstrated the best performance, with AUCs of 0.933 (training), 0.891 (internal validation), and 0.856 (external validation). Calibration and Brier scores confirmed good agreement, and DCA indicated clinical benefit across 10–30% threshold probabilities. SHAP visualizations revealed feature importance hierarchies and clarified the decision logic for individual predictions.</p> Conclusions <p>By integrating IFSCI with SHAP-based explanations, this study provides a transparent, high-performance multimodal framework for early recurrence prediction in ICC, facilitating clinically trustworthy and interpretable decision support.</p>

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A novel interpretable machine learning framework integrating clinicopathological and radiomic features for early recurrence prediction in mass-forming intrahepatic cholangiocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Xiao-li Deng,
  • Chongze Yang,
  • Lan-hui Qin,
  • Xue-feng Lin,
  • Fen Zhong,
  • Jin-yuan Liao

摘要

Rationale and objectives

Early recurrence after curative resection remains a major determinant of poor prognosis in intrahepatic cholangiocarcinoma (ICC). Existing multimodal prediction models often lack interpretability due to feature interference during fusion. This study aimed to develop and externally validate an interpretable multimodal machine-learning model using a novel Independent Feature Selection and Consistent Integration (IFSCI) framework, combined with SHAP-based explanation to enhance transparency.

Materials and methods

A total of 264 patients with mass-forming ICC who underwent radical resection were retrospectively enrolled from two centers. Clinical and CT-based radiomics features were independently selected within each modality using statistical testing and LASSO under the IFSCI design, ensuring modality-specific interpretability before consistent integration. Support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were used to construct clinical, radiomics, and combined models. Model performance was evaluated using AUC, calibration curves, Brier score, and decision curve analysis (DCA). SHAP values were applied to provide global and case-level interpretability.

Results

Six clinical and twenty radiomics features were retained. The MLP-based combined model demonstrated the best performance, with AUCs of 0.933 (training), 0.891 (internal validation), and 0.856 (external validation). Calibration and Brier scores confirmed good agreement, and DCA indicated clinical benefit across 10–30% threshold probabilities. SHAP visualizations revealed feature importance hierarchies and clarified the decision logic for individual predictions.

Conclusions

By integrating IFSCI with SHAP-based explanations, this study provides a transparent, high-performance multimodal framework for early recurrence prediction in ICC, facilitating clinically trustworthy and interpretable decision support.