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Objectives <p>Lung cancer is the leading cause of cancer-related deaths worldwide, with most patients diagnosed at advanced stages. Accurate differentiation of benign and malignant pulmonary nodules remains a major clinical challenge.</p> Materials and methods <p>We established and validated the perinodular vessel count (PVC) as an instrumental imaging biomarker, demonstrating its significant contribution to discriminating malignant pulmonary nodules. Leveraging this finding, we constructed an integrated predictive model incorporating intranodular and perinodular radiomics, PVC, and relevant clinical variables. A two-tiered feature selection strategy employing both maximum relevance minimum redundancy (mRMR) and Relief algorithms was implemented to refine feature sets, followed by the development of an ensemble decision tree-based classifier. The model underwent rigorous multi-center validation.</p> Results <p>The clinical and conventional imaging (CCI) model incorporating perinodular vascular features (AUC = 0.8178, CI: [0.6417,0.9676]) significantly outperformed the non-vascular feature model (AUC = 0.7389, CI: [0.5448,0.9076]). Furthermore, the CCI_Intranodular_Perinodular_Radiomics (CIPR) model demonstrated substantially improved performance over the CCI model, achieving AUCs of 0.8704 (CI: [0.6417,0.9676]) validation set, 0.8225 on independent test set (CI: [0.7298,0.9168]), and 0.7937 (CI: [0.4234,1]) on external test set. Notably, the diagnostic performance of the final model was on par with that of three experienced clinicians. The PVC feature was consistently identified as one of the most important feature among all features in both feature selection and SHapley Additive exPlanations (SHAP) interpretability analysis.</p> Conclusion <p>Integration of vascular characteristics markedly improves diagnostic performance and model generalizability. The consistent importance of PVC highlights its clinical value, and the model shows promising potential to assist in decision-making and reduce unnecessary invasive procedures.</p>

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Improving risk stratification of pulmonary nodules: an integrated perinodular vascular and radiomic model for clinical decision support

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Wen Qiu,
  • Chunyi Liang,
  • Wenxuan Luo,
  • Jinxiu Lin,
  • Jianli Cao,
  • Peng Peng,
  • Yeheng Zhou,
  • Yifan Hu,
  • Renzheng Chen

摘要

Objectives

Lung cancer is the leading cause of cancer-related deaths worldwide, with most patients diagnosed at advanced stages. Accurate differentiation of benign and malignant pulmonary nodules remains a major clinical challenge.

Materials and methods

We established and validated the perinodular vessel count (PVC) as an instrumental imaging biomarker, demonstrating its significant contribution to discriminating malignant pulmonary nodules. Leveraging this finding, we constructed an integrated predictive model incorporating intranodular and perinodular radiomics, PVC, and relevant clinical variables. A two-tiered feature selection strategy employing both maximum relevance minimum redundancy (mRMR) and Relief algorithms was implemented to refine feature sets, followed by the development of an ensemble decision tree-based classifier. The model underwent rigorous multi-center validation.

Results

The clinical and conventional imaging (CCI) model incorporating perinodular vascular features (AUC = 0.8178, CI: [0.6417,0.9676]) significantly outperformed the non-vascular feature model (AUC = 0.7389, CI: [0.5448,0.9076]). Furthermore, the CCI_Intranodular_Perinodular_Radiomics (CIPR) model demonstrated substantially improved performance over the CCI model, achieving AUCs of 0.8704 (CI: [0.6417,0.9676]) validation set, 0.8225 on independent test set (CI: [0.7298,0.9168]), and 0.7937 (CI: [0.4234,1]) on external test set. Notably, the diagnostic performance of the final model was on par with that of three experienced clinicians. The PVC feature was consistently identified as one of the most important feature among all features in both feature selection and SHapley Additive exPlanations (SHAP) interpretability analysis.

Conclusion

Integration of vascular characteristics markedly improves diagnostic performance and model generalizability. The consistent importance of PVC highlights its clinical value, and the model shows promising potential to assist in decision-making and reduce unnecessary invasive procedures.