18F-FDG PET/CT combined with CT three-dimensional reconstruction in the differentiation of benign and malignant pulmonary nodules: A retrospective cohort study
- VernacularTitle:18F-FDG PET/CT联合CT 三维重建鉴别肺结节良恶性的回顾性队列研究
- Author:
Yong CHEN
1
;
Jun WU
2
,
3
;
Shichun LU
2
,
3
;
Chao SUN
2
,
3
;
Yusheng SHU
2
,
3
;
Xiaolin WANG
2
,
3
Author Information
1. Dalian Medical University, Dalian, 116044, Liaoning, P. R. China
2. Department of Thoracic Surgery, Northern Jiangsu People'
3. s Hospital, Yangzhou, 225009, Jiangsu, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary nodule;
three-dimensional reconstruction;
positron emission tomography/computed tomography;
logistic model;
artificial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(03):357-363
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the accuracy of 18F-FDG positron emission tomography/computed tomography (PET/CT) combined with CT three-dimensional reconstruction (CT-3D) in the differential diagnosis of benign and malignant pulmonary nodules. Methods The clinical data of patients who underwent pulmonary nodule surgery in the Department of Thoracic Surgery, Northern Jiangsu People's Hospital from July 2020 to August 2021 were retrospectively analyzed. The preoperative 18F-FDG PET/CT and chest enhanced CT-3D and other imaging data were extracted. The parameters with diagnostic significance were screened by the area under the receiver operating characteristic (ROC) curve (AUC). Three prediction models, including PET/CT prediction model (MOD PET), CT-3D prediction model (MOD CT-3D), and PET/CT combined CT-3D prediction model (MOD combination), were established through binary logistic regression, and the diagnostic performance of the models were validated by ROC curve. Results A total of 125 patients were enrolled, including 57 males and 68 females, with an average age of 61.16±8.57 years. There were 46 patients with benign nodules, and 79 patients with malignant nodules. A total of 2 PET/CT parameters and 5 CT-3D parameters were extracted. Two PET/CT parameters, SUVmax≥1.5 (AUC=0.688) and abnormal uptake of hilar/mediastinal lymph node metabolism (AUC=0.671), were included in the regression model. Among the CT-3D parameters, CT value histogram peaks (AUC=0.694) and CT-3D morphology (AUC=0.652) were included in the regression model. Finally, the AUC of the MOD PET was verified to be 0.738 [95%CI (0.651, 0.824)], the sensitivity was 74.7%, and the specificity was 60.9%; the AUC of the MOD CT-3D was 0.762 [95%CI (0.677, 0.848)], the sensitivity was 51.9%, and the specificity was 87.0%; the AUC of the MOD combination was 0.857 [95%CI (0.789, 0.925)], the sensitivity was 77.2%, the specificity was 82.6%, and the differences were statistically significant (P<0.001). Conclusion 18F-FDG PET/CT combined with CT-3D can improve the diagnostic performance of pulmonary nodules, and its specificity and sensitivity are better than those of single imaging diagnosis method. The combined prediction model is of great significance for the selection of surgical timing and surgical methods for pulmonary nodules, and provides a theoretical basis for the application of artificial intelligence in the pulmonary nodule diagnosis.
