Scholarlink

Background <p>Accurate prediction of metachronous liver metastasis (MLM) within the 24&#xa0;months remains a clinical challenge in rectal cancer. While radiomics offers noninvasive insights into tumor characteristics, few studies have investigated multi-sequence MRI-based habitat radiomics with interpretable modeling strategies.</p> Methods <p>This retrospective study enrolled 391 patients with pathologically confirmed rectal cancer. K-means clustering was applied to pretreatment T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MRI to generate tumor subregions. Radiomic features were extracted from both sequences, and clinical variables were also included. Support vector machine (SVM) classifiers were used to construct radiomics, habitat, and combined models. Model performance was assessed using area under the ROC curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to interpret the contribution of individual features.</p> Results <p>The habitat model demonstrated superior predictive performance compared to conventional radiomics, achieving AUCs of 0.875 in the training cohort, 0.829 in the internal validation cohort, and 0.810 in the external test cohort. The combined model, incorporating clinical variables and habitat features, achieved the highest performance in the validation cohort (AUC = 0.870) and external test cohort (AUC = 0.862). SHAP analysis revealed complementary contributions from T1WI and T2WI features, highlighting the intratumoral heterogeneity interpretability of the multi-sequence habitat approach.</p> Conclusion <p>Multi-sequence MRI-based habitat radiomics demonstrated strong performance in predicting MLM, and the integration with clinical variables further improved accuracy, providing a practical tool for individualized risk assessment and treatment planning.</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

Interpretable habitat radiomics model based on multi-sequence MRI for risk prediction of metachronous liver metastasis in rectal cancer: a multicenter study

  • 期刊论文

  • 发表日期：

    2025年10月16日

查看全文
  • Gouling Zhan,
  • Endong Zhao,
  • Xuehuan Liu,
  • Xiao Gao,
  • Dahe Zhan,
  • Zhibo Zhou,
  • Zuoxi Li,
  • Jun Liu

摘要

Background

Accurate prediction of metachronous liver metastasis (MLM) within the 24 months remains a clinical challenge in rectal cancer. While radiomics offers noninvasive insights into tumor characteristics, few studies have investigated multi-sequence MRI-based habitat radiomics with interpretable modeling strategies.

Methods

This retrospective study enrolled 391 patients with pathologically confirmed rectal cancer. K-means clustering was applied to pretreatment T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MRI to generate tumor subregions. Radiomic features were extracted from both sequences, and clinical variables were also included. Support vector machine (SVM) classifiers were used to construct radiomics, habitat, and combined models. Model performance was assessed using area under the ROC curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to interpret the contribution of individual features.

Results

The habitat model demonstrated superior predictive performance compared to conventional radiomics, achieving AUCs of 0.875 in the training cohort, 0.829 in the internal validation cohort, and 0.810 in the external test cohort. The combined model, incorporating clinical variables and habitat features, achieved the highest performance in the validation cohort (AUC = 0.870) and external test cohort (AUC = 0.862). SHAP analysis revealed complementary contributions from T1WI and T2WI features, highlighting the intratumoral heterogeneity interpretability of the multi-sequence habitat approach.

Conclusion

Multi-sequence MRI-based habitat radiomics demonstrated strong performance in predicting MLM, and the integration with clinical variables further improved accuracy, providing a practical tool for individualized risk assessment and treatment planning.