Circulating tumor cells-based model for pulmonary solid nodules diagnosis: a multicenter study
10.3760/cma.j.cn112434-20220323-00087
- VernacularTitle:基于循环肿瘤细胞的肺实性结节良恶性鉴别预测模型的建立与验证：一项多中心研究
- Author:
Xiang MA
1
;
Shuo SUN
;
Hua HE
;
Mengmeng ZHAO
;
Chang CHEN
;
Shangqing XU
;
Minjie MA
;
Biao HAN
Author Information
1. 兰州大学第一医院胸外科，兰州 730030
- Keywords:
Solid nodules;
Clinical radiological features;
Circulating tumor cell
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2023;39(5):296-302
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the clinical radiological features combined with circulating tumor cells in the diagnosis of benign and malignant pulmonary solid nodules.Methods:Clinical data of 437 patients from Shanghai Pulmonary Hospital(SPH cohort) from January to April 2021 and 82 patients from Lanzhou University First Hospital (LZH cohort) from August 2019 to May 2022 were retrospectively included. Patients in Shanghai pulmonary hospital were randomly divided into training set and internal validation set in a ratio of 4∶1 by random number table method and patients in Lanzhou University First Hospital were as external validation set. Independent risk factors were selected by regression analysis of training set constructed a Nomogram prediction model. The performance of the Nomogram prediction model was estimated by applying receiver operating curve( ROC) analysis, tested in different nodules size and intermediate risk IPSNs and tested by calibration curve. Results:Independent risk factors selected by regression analysis for solid pulmonary nodules were age, the level of CTC, pleural Indentation, lobulation, spiculation. The Nomogram prediction mode provided an area under ROC( AUC) of 0.888, 0.833 in internal validation set and external validation set, outperforming radiological features model(0.835, P=0.007; 0.804, P=0.043) Mayo clinical model(0.781, P=0.019; 0.726, P=0.033) and CTCs(0.699, P=0.002; 0.648, P=0.012) in both two validation sets, C-index of 0.888, 0.871 and corrected C-index of 0.853, 0.842 in both two validation sets . The AUC of the prediction model with internal validation set was 0.905 and 0.871 for nodule diameter of 5-20 mm and intermediate risk probability. Conclusion:The prediction model in this study has better diagnostic value and practicability, and is more effective in clinical diagnosis of diseases.
