European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2026
Prediction of recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma based on CT radiomics model: A multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Yao Yubin, Zhao Yan, Yuan Qiulang, Li Xinyi, Chen Simin, Jian Lian, Li Huiling, Chen Siqin, Lai Bo, Xu Zhuangyong, Zhang Shuixing, Bu Jun, Li Yangkang, Lin Daiying, Mo Xiaokai
Аннотация
OBJECTIVE: Recurrent laryngeal nerve lymph nodes (RLNLN) dissection in resectable esophageal squamous cell carcinoma (ESCC) is challenging due to increased post-operative complications and unfavorable outcomes. We aimed to develop and validate a CT-based radiomic model to predict RLNLN metastasis in ESCC patients to optimize treatment strategies.
METHODS: We retrospectively enrolled 645 ESCC patients from four centers (2015-2022) with pathologically confirmed RLNLN status, stratified into a training cohort, an internal validation cohort, and two external validation cohorts to ensure model generalizability. The radiomics model was built from both non-contrast and contrast-enhanced CT images. The CT-based combined model was developed by integrating the radiomic model with clinicopathological factors (including T/N stage, tumor location, and lymph node size) through multivariate logistic regression. The model's performance was evaluated for discriminative ability, calibration, and clinical usefulness.
RESULTS: Incorporating ten features, the radiomics model effectively distinguished ESCC patients with RLNLN metastasis from those without, achieving AUCs of 0.819 in training, 0.708 in internal validation, and 0.806 and 0.635 in the two external validation cohorts. The combined model demonstrated strong discriminative ability, achieving AUCs of 0.866 in the training cohort, 0.749 in the internal validation cohort, and 0.710 and 0.694 in the two external validation cohorts. Decision curve analysis validated the clinical utility of the combined model across various threshold probabilities, highlighting its potential to inform treatment decisions in ESCC patients.
CONCLUSION: The CT-based combined model demonstrated robust predictive performance for RLNLN metastasis in resectable ESCC across multiple centers, providing clinically actionable support for personalized therapeutic decision-making.
METHODS: We retrospectively enrolled 645 ESCC patients from four centers (2015-2022) with pathologically confirmed RLNLN status, stratified into a training cohort, an internal validation cohort, and two external validation cohorts to ensure model generalizability. The radiomics model was built from both non-contrast and contrast-enhanced CT images. The CT-based combined model was developed by integrating the radiomic model with clinicopathological factors (including T/N stage, tumor location, and lymph node size) through multivariate logistic regression. The model's performance was evaluated for discriminative ability, calibration, and clinical usefulness.
RESULTS: Incorporating ten features, the radiomics model effectively distinguished ESCC patients with RLNLN metastasis from those without, achieving AUCs of 0.819 in training, 0.708 in internal validation, and 0.806 and 0.635 in the two external validation cohorts. The combined model demonstrated strong discriminative ability, achieving AUCs of 0.866 in the training cohort, 0.749 in the internal validation cohort, and 0.710 and 0.694 in the two external validation cohorts. Decision curve analysis validated the clinical utility of the combined model across various threshold probabilities, highlighting its potential to inform treatment decisions in ESCC patients.
CONCLUSION: The CT-based combined model demonstrated robust predictive performance for RLNLN metastasis in resectable ESCC across multiple centers, providing clinically actionable support for personalized therapeutic decision-making.