Application value of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules
10.13491/j.issn.1004-714X.2024.03.020
- VernacularTitle:CT影像组学对亚厘米实性肺结节良恶性鉴别的应用价值
- Author:
Jianing LIU
1
;
Linlin QI
1
;
Jiaqi CHEN
1
;
Fenglan LI
1
;
Shulei CUI
1
;
Sainan CHENG
1
;
Yawen WANG
1
;
Zhen ZHOU
2
;
Jianwei WANG
1
Author Information
1. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China.
2. Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing 100089 China.
- Publication Type:OriginalArticles
- Keywords:
Lung neoplasms;
Artificial intelligence;
Radiomics;
Diagnosis and differentiation;
Computed tomography
- From:
Chinese Journal of Radiological Health
2024;33(3):340-345
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the application efficiency and potential of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules. Methods A retrospective study was performed on the sub-centimeter ( ≤ 10 mm) solid pulmonary nodules detected by enhanced CT in our hospital from March 2020 to January 2023. Malignancy was confirmed by surgical pathology, and benignity was confirmed by surgical pathology or follow-up. Lesions were manually segmented and radiomic features were extracted. The feature dimension was reduced via feature correlation analysis and least absolute shrinkage and selection operator (LASSO). The 5-fold cross validation was used to validate the model. Support vector machine, logistic regression, linear classification support vector machine, gradient boosting, and random forest models were established for CT radiomics. Receiver operating characteristic curves were drawn. Delong test was used to compare the diagnostic performance of the five classifiers. The optimal model was selected and compared to radiologists with medium and high seniority. Results A total of 303 nodules, 136 of which were malignant, were examined. Radiomics models were established after feature extraction and selection. On test set, the areas under the receiver operating characteristic curves of support vector machine, logistic regression, linear classification support vector machine, random forest, and gradient boosting models were 0.922 (95%CI: 0.893, 0.950), 0.910 (95%CI: 0.878, 0.942), 0.905 (95%CI: 0.872, 0.938), 0.899 (95%CI: 0.865, 0.933), and 0.896 (95%CI: 0.862, 0.930), respectively. Delong test indicated no significant differences in the performance of the five radiomics models, and the support vector machine model showed the highest accuracy and F1 score. The support vector machine model showed significantly higher diagnostic accuracy as compared to radiologists (83.8% vs. 55.4%, P < 0.001). Conclusion The radiomics models achieved high diagnostic efficiency and may help to reduce the uncertainty in diagnosis of malignant and benign sub-centimeter solid nodules by radiologists.
