Yang Ling, Ding Yingying, Zhang Dafu, Yang Guangjun, Dong Xingxiang, Zhang Zhiping, Zhang Caixia, Zhang Wenjie, Dai Youguo, Li Zhenhui
Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China.
Department of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China.
BMC Med Imaging. 2025 Feb 3;25(1):36. doi: 10.1186/s12880-025-01577-5.
A predictive model was developed based on enhanced computed tomography (CT), laboratory test results, and pathological indicators to achieve the convenient and effective prediction of single lymph node metastasis (LNM) in gastric cancer.
Sixty-six consecutive patients (235 regional lymph nodes) with pathologically confirmed gastric cancer who underwent surgery at our hospital between December 2020 and November 2021 were retrospectively reviewed. They were randomly allocated to training (n = 38, number of lymph nodes = 119) and validation (n = 28, number of lymph nodes = 116) datasets. The clinical data, laboratory test results, enhanced CT characteristics, and pathological indicators from gastroscopy-guided needle biopsies were obtained. Multivariable logistic regression with generalised estimation equations (GEEs) was used to develop a predictive model for LNM in gastric cancer. The predictive performance of the model developed using the training and validation datasets was validated using receiver operating characteristic curves.
Lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter were independent predictors of LNM in gastric cancer (p < 0.01). The GEE-logistic model was associated with LNM (p = 0.001). The area under the curve and accuracy of the model, with 95% confidence intervals, were 0.944 (0.890-0.998) and 0.897 (0.813-0.952), respectively, in the training dataset and 0.836 (0.751-0.921) and 0.798 (0.699-0.876), respectively, in the validation dataset.
The predictive model constructed based on lymph node enhancement pattern, Ki67 level, and lymph node long-axis diameter exhibited good performance in predicting LNM in gastric cancer and should aid the lymph node staging of gastric cancer and clinical decision-making.
基于增强计算机断层扫描（CT）、实验室检查结果和病理指标开发一种预测模型，以实现对胃癌单个淋巴结转移（LNM）的便捷有效预测。
回顾性分析2020年12月至2021年11月间在我院接受手术的66例经病理证实的胃癌连续患者（235个区域淋巴结）。将他们随机分配到训练数据集（n = 38，淋巴结数量 = 119）和验证数据集（n = 28，淋巴结数量 = 116）。获取临床数据、实验室检查结果、增强CT特征以及胃镜引导下针吸活检的病理指标。使用广义估计方程（GEEs）的多变量逻辑回归来开发胃癌LNM的预测模型。使用受试者工作特征曲线验证使用训练和验证数据集开发的模型的预测性能。
淋巴结强化模式、Ki67水平和淋巴结长轴直径是胃癌LNM的独立预测因素（p < 0.01）。GEE逻辑模型与LNM相关（p = 0.001）。在训练数据集中，模型的曲线下面积和准确率及其95%置信区间分别为0.944（0.890 - 0.998）和0.897（0.813 - 0.952），在验证数据集中分别为0.836（0.751 - 0.921）和0.798（0.699 - 0.876）。
基于淋巴结强化模式、Ki67水平和淋巴结长轴直径构建 的预测模型在预测胃癌LNM方面表现良好，应有助于胃癌的淋巴结分期和临床决策。
