Comprehensive evaluation of benign and malignant pulmonary nodules using combined biological testing and imaging assessment in 1 017 patients: A retrospective cohort study
- VernacularTitle:生物学检测与影像学综合评估1 017例肺部结节良恶性的回顾性队列研究
- Author:
Lei ZHANG
1
;
Zihao LI
2
;
Nan LI
3
;
Jun CHENG
2
;
Feng ZHANG
3
;
Pinghui XIA
2
;
Wang LÜ
2
;
Jian HU
2
Author Information
1. Cadre Health Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China
2. Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China
3. Health Management Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, P. R. China
- Publication Type:Journal Article
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(01):60-66
- CountryChina
- Language:Chinese
-
Abstract:
Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.
