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Prediction of recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma based on CT
Yubin Yao1, Yan Zhao2, Qiulang Yuan3
1Department of Radiology, Shantou Central Hospital, Shantou Clinical Medical College of Jinan University, No.114 Waima Road, Shantou, People's Republic of China; Department of Radiology, The First Affiliated Hospital of Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, Guangdong, People's Republic of China.
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.