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Objectives <p>This study aimed to develop a novel radiomic model by incorporating features from both habitat subregions and peritumoral regions to preoperatively predict lymph node metastasis (LNM) in early-stage cervical cancer using diffusion-weighted imaging (DWI).</p> Methods <p>433 early-stage cervical cancer patients from four hospitals undergoing DWI were enrolled. Peritumoral regions were delineated by 1–4&#xa0;mm expansion, and habitat analysis identified two intratumoral subregions, named Habitat 1 and Habitat 2 respectively. Intratumoral, peritumoral, and habitat features were extracted for model development. Prediction models included: Intra, Peri 1–4&#xa0;mm, Habitat (1 and 2), and Fusion model. Performance was assessed via receiver operating characteristic curve, calibration, and decision curve analyses.</p> Results <p>Among the peritumoral models, the 3 mm peritumoral model demonstrated the best performance for LNM prediction, with AUCs of 0.867 (95% CI: 0.805–0.929), 0.747 (95% CI: 0.608–0.886), and 0.815 (95% CI: 0.743–0.887) in the training, validation, and test set, respectively. The Habitat 1 model also showed favorable performance, achieving AUCs of 0.838 (95% CI: 0.774–0.901), 0.712 (95% CI: 0.556–0.867), and 0.782 (95% CI: 0.694–0.869) in the training, validation, and test groups, respectively. Notably, Fusion model, combining Peri 3&#xa0;mm and Habitat 1 features, achieved the best overall performance, with AUCs of 0.910 (95% CI: 0.868–0.953), 0.747 (95% CI: 0.600–0.894), and 0.837 (95% CI: 0.767–0.907) across the training, validation, and test sets, respectively and outperformed other models in calibration and decision curve analyses.</p> Conclusion <p>The Fusion model enables superior and noninvasive prediction of LNM in early-stage cervical cancer patients.</p>

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DWI-derived intratumoral, peritumoral, and habitat features for preoperative prediction of lymph node metastasis in early-stage cervical cancer using machine learning method

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Tao Yang,
  • Tianhui Zhang,
  • Weihao Yan,
  • Xian Chen,
  • Shujian Li,
  • Zhijun Ye,
  • zi Yang,
  • zhihan Yan,
  • Xue Wang

摘要

Objectives

This study aimed to develop a novel radiomic model by incorporating features from both habitat subregions and peritumoral regions to preoperatively predict lymph node metastasis (LNM) in early-stage cervical cancer using diffusion-weighted imaging (DWI).

Methods

433 early-stage cervical cancer patients from four hospitals undergoing DWI were enrolled. Peritumoral regions were delineated by 1–4 mm expansion, and habitat analysis identified two intratumoral subregions, named Habitat 1 and Habitat 2 respectively. Intratumoral, peritumoral, and habitat features were extracted for model development. Prediction models included: Intra, Peri 1–4 mm, Habitat (1 and 2), and Fusion model. Performance was assessed via receiver operating characteristic curve, calibration, and decision curve analyses.

Results

Among the peritumoral models, the 3 mm peritumoral model demonstrated the best performance for LNM prediction, with AUCs of 0.867 (95% CI: 0.805–0.929), 0.747 (95% CI: 0.608–0.886), and 0.815 (95% CI: 0.743–0.887) in the training, validation, and test set, respectively. The Habitat 1 model also showed favorable performance, achieving AUCs of 0.838 (95% CI: 0.774–0.901), 0.712 (95% CI: 0.556–0.867), and 0.782 (95% CI: 0.694–0.869) in the training, validation, and test groups, respectively. Notably, Fusion model, combining Peri 3 mm and Habitat 1 features, achieved the best overall performance, with AUCs of 0.910 (95% CI: 0.868–0.953), 0.747 (95% CI: 0.600–0.894), and 0.837 (95% CI: 0.767–0.907) across the training, validation, and test sets, respectively and outperformed other models in calibration and decision curve analyses.

Conclusion

The Fusion model enables superior and noninvasive prediction of LNM in early-stage cervical cancer patients.