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Objectives <p>To develop an intratumor heterogeneity (ITH)-aware deep learning (DL) framework for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC).</p> Methods <p>This multi-center retrospective study, which included 381 patients, proposed a ITH-aware DL framework integrated with adaptive habitat mapping to automatically decode spatial ITH from primary tumor contrast-enhanced CT images.The predictive performance of the ITH-aware DL model was compared against conventional radiomics and 3D DL model. A stacking ensemble model combining these three methods was also constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The prognostic value for overall survival (OS) stratification was evaluated.</p> Results <p>The ITH-aware DL model achieved superior preoperative LNM prediction in external testing (AUC: 0.745–0.766), outperforming both radiomics (AUC: 0.682–0.694) and 3D DL models (AUC: 0.718–0.732). The stacking ensemble integrating these models attained the highest diagnostic accuracy (AUC: 0.786–0.804). Critically, both the ITH-aware DL and stacking models effectively stratified patients into distinct risk groups with significantly different OS outcomes.</p> Conclusion <p>The proposed ITH-aware DL framework provides a robust, automated tool for preoperative LNM prediction in LSCC. By enabling accurate risk stratification, it holds significant potential to inform personalized surgical planning and improve treatment strategies.</p>

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Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Wencan Wang,
  • Lingyu Zhang,
  • Junhong Zhang,
  • Xinyu Li,
  • Wenfeng Wei,
  • Li Liang,
  • Shiwen Li,
  • Haomiao Ma,
  • Xuhui Zhao,
  • Qian Zhang,
  • Huina Guo,
  • Yanhong Hao,
  • Hua Zhang,
  • Feng Gao,
  • Jianxin Zhang,
  • Lingjie Wang

摘要

Objectives

To develop an intratumor heterogeneity (ITH)-aware deep learning (DL) framework for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC).

Methods

This multi-center retrospective study, which included 381 patients, proposed a ITH-aware DL framework integrated with adaptive habitat mapping to automatically decode spatial ITH from primary tumor contrast-enhanced CT images.The predictive performance of the ITH-aware DL model was compared against conventional radiomics and 3D DL model. A stacking ensemble model combining these three methods was also constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The prognostic value for overall survival (OS) stratification was evaluated.

Results

The ITH-aware DL model achieved superior preoperative LNM prediction in external testing (AUC: 0.745–0.766), outperforming both radiomics (AUC: 0.682–0.694) and 3D DL models (AUC: 0.718–0.732). The stacking ensemble integrating these models attained the highest diagnostic accuracy (AUC: 0.786–0.804). Critically, both the ITH-aware DL and stacking models effectively stratified patients into distinct risk groups with significantly different OS outcomes.

Conclusion

The proposed ITH-aware DL framework provides a robust, automated tool for preoperative LNM prediction in LSCC. By enabling accurate risk stratification, it holds significant potential to inform personalized surgical planning and improve treatment strategies.