Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
Intratumor habitat aware deep learning framework for preoperative lymph node metastasis prediction in laryngeal carcinoma.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Wang Wencan, Zhang Lingyu, Zhang Junhong, Li Xinyu, Wei Wenfeng, Liang Li, Li Shiwen, Ma Haomiao, Zhao Xuhui, Zhang Qian, Guo Huina, Hao Yanhong, Zhang Hua, Gao Feng, Zhang Jianxin, Wang Lingjie
Аннотация
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.
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.