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Background <p>Lymphovascular invasion (LVI) status in rectal cancer (RC) without lymph node metastasis (LNM) can significantly influence the patient’s treatment decisions. This study aims to develop and validate a combined model based on MRI radiomics features integrated with clinical immune-inflammatory biomarkers for the prediction of LVI status in RC without LNM. The Shapley Additive Explanation (SHAP) method was employed to visualize the prediction process and enhance interpretability for clinical application.</p> Methods <p>We retrospectively collected data from 257 RC patients without LNM from two centers. Univariate and multivariate logistic regression analyses were performed on clinical data to identify independent predictors of LVI. Volumes of interest were manually delineated on T2WI and ADC sequences, and corresponding radiomic features were extracted. A combined model was constructed by combining rad-score and clinical immune-inflammatory biomarkers, and the SHAP was used to visualize the prediction process.</p> Results <p>The area under the curve (AUC) of the combined model was based on intratumoral features (training vs. testing vs. validation datasets: 0.813 vs. 0.854 vs. 0.807). The AUC of the combined model was based on both intra- and peritumoral features (training vs. testing vs. validation datasets: 0.855 vs. 0.841 vs. 0.860). After comparison, the combined model (C + Q) based on intra- and peritumoral MRI radiomics features integrated with clinical immune-inflammatory biomarkers demonstrated better predictive performance.</p> Conclusion <p>The combined model (C + Q) has great potential in the non-invasive prediction of LVI in RC without LNM, providing a basis for stratified management and individualized treatment decisions for RC patients.</p>

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Noninvasive prediction of lymphovascular invasion in rectal cancer without lymph node metastasis using a SHAP-interpretable combined model integrating MRI radiomics features and clinical immune-inflammatory biomarkers: a bicenter study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月30日

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  • Leping Peng,
  • Jingjing Niu,
  • Gang Huang,
  • Fan Zhang,
  • Fang Ma,
  • Xiuling Zhang,
  • Yu Wang,
  • Kai Ai,
  • Xiaoyue Zhang,
  • Yuqi He,
  • Wei Cai,
  • Xiaona Zuo,
  • Yingmei Jia,
  • Shuhong Gao,
  • Yuan-Cheng Wang,
  • Lili Wang

摘要

Background

Lymphovascular invasion (LVI) status in rectal cancer (RC) without lymph node metastasis (LNM) can significantly influence the patient’s treatment decisions. This study aims to develop and validate a combined model based on MRI radiomics features integrated with clinical immune-inflammatory biomarkers for the prediction of LVI status in RC without LNM. The Shapley Additive Explanation (SHAP) method was employed to visualize the prediction process and enhance interpretability for clinical application.

Methods

We retrospectively collected data from 257 RC patients without LNM from two centers. Univariate and multivariate logistic regression analyses were performed on clinical data to identify independent predictors of LVI. Volumes of interest were manually delineated on T2WI and ADC sequences, and corresponding radiomic features were extracted. A combined model was constructed by combining rad-score and clinical immune-inflammatory biomarkers, and the SHAP was used to visualize the prediction process.

Results

The area under the curve (AUC) of the combined model was based on intratumoral features (training vs. testing vs. validation datasets: 0.813 vs. 0.854 vs. 0.807). The AUC of the combined model was based on both intra- and peritumoral features (training vs. testing vs. validation datasets: 0.855 vs. 0.841 vs. 0.860). After comparison, the combined model (C + Q) based on intra- and peritumoral MRI radiomics features integrated with clinical immune-inflammatory biomarkers demonstrated better predictive performance.

Conclusion

The combined model (C + Q) has great potential in the non-invasive prediction of LVI in RC without LNM, providing a basis for stratified management and individualized treatment decisions for RC patients.