AUTHOR=Huang Xiaoli , Jiang Shumin , Li Zhe , Lin Xiong , Chen Zhipeng , Hu Chao , He Jianbing , Yan Chun , Duan Hongbing , Ke Sunkui 

TITLE=Prediction of right recurrent laryngeal nerve lymph node metastasis in esophageal cancer based on computed tomography imaging histology

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1388355

DOI=10.3389/fonc.2024.1388355

ISSN=2234-943X

ABSTRACT=PurposeThis study aimed to identify risk factors for right recurrent laryngeal nerve lymph node (RRLNLN) metastasis using computed tomography (CT) imaging histology and clinical data from patients with esophageal squamous cell carcinoma (ESCC), ultimately developing a clinical prediction model.MethodsData were collected from 370 patients who underwent surgical resection at the Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, from December 2014 to December 2020. Subsequently, the venous-stage chest-enhanced CT images of the patients were imported into 3DSlicer 4.11 software, allowing for the extraction of imaging histological features. Additionally, by combining the clinical data of the patients, single- and multifactor analyses were conducted to screen the risk factors and build a predictive model in the form of a nomogram. The area under the curve (AUC) was used as a discriminant for model accuracy, while differentiation and calibration methods were applied to further evaluate the model’s accuracy. Finally, the Bootstrap resampling method was employed to repeat sampling 2,000 times to draw calibration curves, while the K-fold crossvalidation method was used for the internal validation of the prediction model.ResultsThe RRLNLN lymph node metastasis rate was 17.3%. Four significant factors—Maximum2DDiameterSlice, Mean, Imc1, and Dependence Entropy—were identified. Alignment diagrams were subsequently constructed, yielding an AUC of 0.938 and a C-index of 0.904 during internal validation.ConclusionThe model demonstrates high predictive accuracy, making it a valuable tool for guiding the development of preoperative protocols.