Scholarlink

Objectives <p>To develop an interpretable radiomics model for predicting 3-year progression-free survival (PFS) in rectal cancer (RC) by integrating consensus molecular subtype (CMS) indicators and Shapley additive explanations (SHAP) analysis with ultra-high b-value diffusion-weighted imaging (DWI).</p> Methods <p>This retrospective study included 153 RC patients randomly assigned to training (<i>n</i> = 107) and validation (<i>n</i> = 46) cohorts. All patients underwent 3.0T DWI with b-values of 0, 1500, 2000, and 3000&#xa0;s/mm<sup>2</sup>. Radiomic features were extracted from stretched exponential model parametric maps. Radscore1 (Conventional radiomic features ) and Radscore2 (CMS surrogate-related features ) were separately calculated with least absolute shrinkage and selection operator Cox regression (LASSO-Cox). Five prognostic models were constructed with clinical factors and Radscore in evaluating 3-year PFS. Model performance was evaluated using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and net reclassification improvement (NRI) analysis.</p> Results <p>In the validation cohort, radscore2 (area under the curve (AUC) = 0.787) outperformed radscore1 (AUC = 0.751) in predicting 3-year PFS. Model 5 (clinical + radscore2) achieved the highest AUC (0.823) with good calibration. NRI analysis showed significant improvement for model 5 compared with the clinical model (NRI = 0.540, <i>p</i> = 0.001). SHAP analysis identified α_logarithm_ngtdm_Contrast (0.463) and α_gradient_firstorder_TotalEnergy (0.445) as the most important features.</p> Conclusion <p>The integration of CMS-surrogate guided feature selection and SHAP analysis could construct an interpretable radiomics model with good performance in predicting 3-year PFS in rectal cancer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prognostic prediction model for rectal cancer based on CMS subtype indicators and SHAP-based interpretable analysis

  • 期刊论文

  • 发表日期：

    2026年6月27日

查看全文
  • Wanqing Li,
  • Wanting Zhao,
  • Xiaoai Qiao,
  • Jianmin Zheng,
  • Xiangyi Du,
  • Yongzhan Nie,
  • Guangwen Zhang,
  • Jinsong Zhang

摘要

Objectives

To develop an interpretable radiomics model for predicting 3-year progression-free survival (PFS) in rectal cancer (RC) by integrating consensus molecular subtype (CMS) indicators and Shapley additive explanations (SHAP) analysis with ultra-high b-value diffusion-weighted imaging (DWI).

Methods

This retrospective study included 153 RC patients randomly assigned to training (n = 107) and validation (n = 46) cohorts. All patients underwent 3.0T DWI with b-values of 0, 1500, 2000, and 3000 s/mm2. Radiomic features were extracted from stretched exponential model parametric maps. Radscore1 (Conventional radiomic features ) and Radscore2 (CMS surrogate-related features ) were separately calculated with least absolute shrinkage and selection operator Cox regression (LASSO-Cox). Five prognostic models were constructed with clinical factors and Radscore in evaluating 3-year PFS. Model performance was evaluated using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and net reclassification improvement (NRI) analysis.

Results

In the validation cohort, radscore2 (area under the curve (AUC) = 0.787) outperformed radscore1 (AUC = 0.751) in predicting 3-year PFS. Model 5 (clinical + radscore2) achieved the highest AUC (0.823) with good calibration. NRI analysis showed significant improvement for model 5 compared with the clinical model (NRI = 0.540, p = 0.001). SHAP analysis identified α_logarithm_ngtdm_Contrast (0.463) and α_gradient_firstorder_TotalEnergy (0.445) as the most important features.

Conclusion

The integration of CMS-surrogate guided feature selection and SHAP analysis could construct an interpretable radiomics model with good performance in predicting 3-year PFS in rectal cancer.