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Objective <p>To develop an interpretable magnetic resonance imaging (MRI)-based framework for preoperative histologic grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) and exploratory prognostic stratification.</p> Materials and methods <p>A retrospective analysis was conducted on preoperative MRI from 333 IMCC patients across three centers (training cohort, <i>n</i> = 240; external validation cohort, <i>n</i> = 93). An ensemble deep learning (DL) framework synergizing 2.5D and 3D ResNet-50 architectures was constructed. Significant variables from clinical-laboratory-imaging (ClinLabImag) features, radiomics, and DL outputs were integrated into a combined model. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Model interpretability was evaluated with Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP), and the Kaplan–Meier method was used to compare overall survival (OS) between risk groups.</p> Results <p>The DL model achieved an external validation AUC of 0.804 (95% confidence interval (CI): 0.712–0.896), significantly outperforming standalone 2.5D (<i>p</i> = 0.025) and 3D architectures (<i>p</i> = 0.030). The Combined model (AUC: 0.843 [95% CI, 0.749–0.938]) showed better external validation performance than the Radiomics (<i>p</i> = 0.019) and ClinLabImag models (<i>p</i> = 0.017), with only modest, non-significant improvement over the DL model (<i>p</i> = 0.355). SHAP analysis showed that DL features contributed most to model predictions. The Combined model showed exploratory OS differences between risk groups.</p> Conclusion <p>An interpretable multidimensional MRI-based DL framework supports noninvasive preoperative grading in IMCC and provides exploratory prognostic information.</p> Critical relevance statement <p>This study critically evaluates an interpretable multidimensional MRI-based deep learning framework for preoperative grading and exploratory prognostic stratification of intrahepatic mass-forming cholangiocarcinoma, supporting individualized radiologic risk assessment before treatment.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Reliable noninvasive MRI biomarkers are needed for preoperative grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma to guide individualized treatment planning.</p> </ItemContent> <ItemContent> <p>The combined multidimensional MRI model achieved favorable external validation performance and showed exploratory overall survival differences between IMCC risk groups.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Multidimensional deep learning for grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Liyong Zhuo,
  • Wenjing Chen,
  • Zijun Song,
  • Lihong Xing,
  • Xiaomeng Li,
  • Jiawei Hao,
  • Zimei Yang,
  • Xuechun Wang,
  • Caiying Li,
  • Jianing Wang,
  • Xiaoping Yin

摘要

Objective

To develop an interpretable magnetic resonance imaging (MRI)-based framework for preoperative histologic grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) and exploratory prognostic stratification.

Materials and methods

A retrospective analysis was conducted on preoperative MRI from 333 IMCC patients across three centers (training cohort, n = 240; external validation cohort, n = 93). An ensemble deep learning (DL) framework synergizing 2.5D and 3D ResNet-50 architectures was constructed. Significant variables from clinical-laboratory-imaging (ClinLabImag) features, radiomics, and DL outputs were integrated into a combined model. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Model interpretability was evaluated with Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP), and the Kaplan–Meier method was used to compare overall survival (OS) between risk groups.

Results

The DL model achieved an external validation AUC of 0.804 (95% confidence interval (CI): 0.712–0.896), significantly outperforming standalone 2.5D (p = 0.025) and 3D architectures (p = 0.030). The Combined model (AUC: 0.843 [95% CI, 0.749–0.938]) showed better external validation performance than the Radiomics (p = 0.019) and ClinLabImag models (p = 0.017), with only modest, non-significant improvement over the DL model (p = 0.355). SHAP analysis showed that DL features contributed most to model predictions. The Combined model showed exploratory OS differences between risk groups.

Conclusion

An interpretable multidimensional MRI-based DL framework supports noninvasive preoperative grading in IMCC and provides exploratory prognostic information.

Critical relevance statement

This study critically evaluates an interpretable multidimensional MRI-based deep learning framework for preoperative grading and exploratory prognostic stratification of intrahepatic mass-forming cholangiocarcinoma, supporting individualized radiologic risk assessment before treatment.

Key Points

Reliable noninvasive MRI biomarkers are needed for preoperative grading and prognostic assessment of intrahepatic mass-forming cholangiocarcinoma to guide individualized treatment planning.

The combined multidimensional MRI model achieved favorable external validation performance and showed exploratory overall survival differences between IMCC risk groups.

Graphical Abstract