Huang Xiaoli, Jiang Shumin, Li Zhe, Lin Xiong, Chen Zhipeng, Hu Chao, He Jianbing, Yan Chun, Duan Hongbing, Ke Sunkui
Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China.
The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, China.
Front Oncol. 2025 Feb 17;14:1388355. doi: 10.3389/fonc.2024.1388355. eCollection 2024.
This 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.
Data 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.
The 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.
The model demonstrates high predictive accuracy, making it a valuable tool for guiding the development of preoperative protocols.
本研究旨在利用计算机断层扫描（CT）成像、组织学及来自食管鳞状细胞癌（ESCC）患者的临床数据，确定右侧喉返神经淋巴结（RRLNLN）转移的危险因素，最终建立一个临床预测模型。
收集2014年12月至2020年12月在厦门大学附属中山医院胸外科接受手术切除的370例患者的数据。随后，将患者的静脉期胸部增强CT图像导入3DSlicer 4.11软件，以提取成像组织学特征。此外，结合患者的临床数据，进行单因素和多因素分析以筛选危险因素，并以列线图的形式建立预测模型。曲线下面积（AUC）用作模型准确性的判别指标，同时采用鉴别和校准方法进一步评估模型的准确性。最后，采用Bootstrap重采样方法重复采样2000次绘制校准曲线，采用K折交叉验证方法对预测模型进行内部验证。
RRLNLN淋巴结转移率为17.3%。确定了四个显著因素——最大二维直径切片、平均值、Imc1和依赖熵。随后构建列线图，内部验证期间AUC为0.938，C指数为0.904。
该模型具有较高的预测准确性，是指导术前方案制定的有价值工具。
