Establishment and validation of a model for predicting infiltration of pulmonary subsolid nodules by circulating tumor cells combined with imaging features
- VernacularTitle:循环肿瘤细胞联合影像学特征预测肺亚实性结节浸润性模型的建立和验证
- Author:
Xiang MA
1
,
2
,
3
,
4
;
Ruijiang LIN
3
,
4
,
5
;
Minjie MA
3
,
4
,
5
;
Xiong CAO
3
,
4
,
5
;
Qiuhao LIANG
1
,
2
,
3
,
4
;
Zhiwei HAN
1
,
2
,
3
,
4
;
Shangqing XU
3
,
4
,
5
;
Biao HAN
1
,
2
,
3
,
4
Author Information
1. 1. The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, P. R. China
2. 2. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China
3. 3. Major in R &
4. D and Application of Key Technologies in Thoracic Surgery Gansu International Science and Technology Cooperation Base, Lanzhou, 730000, P. R. China
5. 2. Department of Thoracic Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary subsolid nodules;
clinical radiological features;
circulating tumor cells;
tumor invasiveness
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2023;30(02):198-204
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the clinical radiological features combined with circulating tumor cells (CTCs) in the diagnosis of invasiveness evaluation of subsolid nodules in lung cancers. Methods Clinical data of 296 patients from the First Hospital of Lanzhou University between February 2019 and February 2021 were retrospectively included. There were 130 males and 166 females with a median age of 62.00 years. Patients were randomly divided into a training set and an internal validation set with a ratio of 3 : 1 by random number table method. The patients were divided into two groups: a preinvasive lesion group (atypical adenomatoid hyperplasia and adenocarcinoma in situ) and an invasive lesion group (microinvasive adenocarcinoma and invasive adenocarcinoma). Independent risk factors were selected by regression analysis of training set and a Nomogram prediction model was constructed. The accuracy and consistency of the model were verified by the receiver operating characteristic curve and calibration curve respectively. Subgroup analysis was conducted on nodules with different diameters to further verify the performance of the model. Specific performance metrics, including sensitivity, specificity, positive predictive value, negative predictive value and accuracy at the threshold were calculated. Results Independent risk factors selected by regression analysis for subsolid nodules were age, CTCs level, nodular nature, lobulation and spiculation. The Nomogram prediction mode provided an area under the curve (AUC) of 0.914 (0.872, 0.956), outperforming clinical radiological features model AUC [0.856 (0.794, 0.917), P=0.003] and CTCs AUC [0.750 (0.675, 0.825), P=0.001] in training set. C-index was 0.914, 0.894 and corrected C-index was 0.902, 0.843 in training set and internal validation set, respectively. The AUC of the prediction model in training set was 0.902 (0.848, 0.955), 0.913 (0.860, 0.966) and 0.873 (0.730, 1.000) for nodule diameter of 5-20 mm, 10-20 mm and 21-30 mm, respectively. Conclusion The prediction model in this study has better diagnostic value, and is more effective in clinical diagnosis of diseases.
