Invasiveness evaluation of pulmonary ground-glass nodules by CT features combined with tumor markers: A retrospective cohort study
- VernacularTitle:CT特征联合肿瘤标志物预测肺磨玻璃结节肿瘤浸润性的回顾性队列研究
- Author:
Hua HE
1
;
Wenteng HU
2
;
Ruijiang LIN
2
;
Ning WEI
2
;
Minjie MA
2
;
Biao HAN
2
Author Information
1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730030, P. R. China
2. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, P. R. China
- Publication Type:Journal Article
- Keywords:
Ground-glass nodules;
CT characteristic;
tumor markers;
tumor invasiveness;
artificial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2022;29(09):1113-1119
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the independent risk factors for tumor invasiveness of ground-glass nodules and establish a tumor invasiveness prediction model. Methods A retrospective analysis was performed in 389 patients with ground-glass nodules admitted to the Department of Thoracic Surgery in the First Hospital of Lanzhou University from June 2018 to May 2021 with definite pathological findings, including clinical data, imaging features and tumor markers. A total of 242 patients were included in the study according to inclusion criteria, including 107 males and 135 females, with an average age of 57.98±9.57 years. CT data of included patients were imported into the artificial intelligence system in DICOM format. The artificial intelligence system recognized, automatically calculated and output the characteristics of pulmonary nodules, such as standard diameter, solid component size, volume, average CT value, maximum CT value, minimum CT value, central CT value, and whether there were lobulation, burr sign, pleural depression and blood vessel passing. The patients were divided into two groups: a preinvasive lesions group (atypical adenomatoid hyperplasia/adenocarcinoma in situ) and an invasive lesions group (minimally invasive adenocarcinoma/ invasive adenocarcinoma). Univariate and multivariate analyses were used to screen the independent risk factors for tumor invasiveness of ground-glass nodules and then a prediction model was established. The receiver operating characteristic (ROC) curve was drawn, and the critical value was calculated. The sensitivity and specificity were obtained according to the Yorden index. Results Univariate and multivariate analyses showed that central CT value, Cyfra21-1, solid component size, nodular nature and burr of the nodules were independent risk factors for the diagnosis of tumor invasiveness of ground-glass nodules. The optimum critical value of the above indicators between preinvasive lesions and invasive lesions were –309.00 Hu, 3.23 ng/mL, 8.65 mm, respectively. The prediction model formula for tumor invasiveness probability was logit (P)=0.982–(3.369×nodular nature)+(0.921×solid component size)+(0.002×central CT value)+(0.526×Cyfra21-1)–(0.095 3×burr). The areas under the curve obtained by plotting the ROC curve using the regression probabilities of regression model was 0.908. The accuracy rate was 91.3%. Conclusion The logistic regression model established in this study can well predict the tumor invasiveness of ground-glass nodules by CT and tumor markers with high predictive value.
