Development of a nomogram prediction model based on 3D quantitative parameters for mediastinal lymph node metastases in clinical stage ⅠA lung adenocarcinoma
10.3969/j.issn.1002-1671.2023.12.006
- VernacularTitle:基于肺结节3D量化参数预测cⅠA期肺腺癌纵隔淋巴结转移的价值
- Author:
Zhixi LI
1
;
Yongjun PAN
;
Zhikang YE
;
Yingjun ZHOU
;
Guoneng CHEN
;
Zhichao ZUO
;
Wei ZHANG
Author Information
1. 广西医科大学附属柳州市人民医院放射科,广西 柳州 545006
- Keywords:
lung adenocarcinoma;
clinical stage ⅠA;
mediastinal lymph node metastases;
nomogram;
computed tomography
- From:
Journal of Practical Radiology
2023;39(12):1936-1940
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a nomogram based on pulmonary nodules preoperative CT signs and 3D quantitative parameters for predicting mediastinal lymph node metastases in patients with clinical stage ⅠA lung adenocarcinoma.Methods The imaging data of 164 patients who underwent preoperative CT scan and systematic lymph node dissection were analyzed retrospectively.Commercially available AI software was used to extract 3D quantitative parameters of pulmonary nodules automatically,and CT signs of pulmonary nodules were analyzed.Logistic regression was used to explore the role of these parameters in predicting pathological nodal involvement.A nomogram prediction model was established,then discrimination and calibration of the model were evaluated.Results Among 164 enrolled patients,19(11.6％)were tested positive for mediastinal lymph node metastases at pathology review.The nomogram incorporated spiculation,lobulation,the largest cross-sectional area,and carcinoembryonic antigen(CEA).The model showed great discrimination and calibration,with a C-index of 0.942[95％confidence interval(CI)0.923-0.961].The predicted value of the model fitted well with the actual observed value on the calibration curve.Conclusion The nomogram prediction model based on preoperative CT signs,3D quantitative parameters,and CEA can estimate the probability of mediastinal lymph node metastases in clinical stage ⅠA lung adenocarcinoma.This model may help with clinical decision-making and individualized evaluation.
