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Machine learning‐based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.
- Source :
- Thoracic Cancer; Feb2024, Vol. 15 Issue 6, p466-476, 11p
- Publication Year :
- 2024
-
Abstract
- Background: Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. Methods: A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical‐radiological semantic features; (2) The radiomics models (radiomics+ and radiomics−) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite−) incorporated both above. Results: In the testing set, the radiomics+/− models and the composite+/− models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/− models and the composite+/− models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/− and composite+/− models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics−, composite+ model vs. composite−) (p > 0.05). Conclusions: The present study established a machine learning‐based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability. [ABSTRACT FROM AUTHOR]
- Subjects :
- TUBERCULOSIS diagnosis
ADENOCARCINOMA
LUNG cancer
SOLITARY pulmonary nodule
MULTIVARIATE analysis
AGE distribution
MACHINE learning
RETROSPECTIVE studies
RADIOMICS
PEARSON correlation (Statistics)
SEX distribution
RESEARCH funding
DESCRIPTIVE statistics
PREDICTION models
LOGISTIC regression analysis
SMOKING
COMPUTED tomography
DATA analysis software
LONGITUDINAL method
EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 17597706
- Volume :
- 15
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Thoracic Cancer
- Publication Type :
- Academic Journal
- Accession number :
- 175605248
- Full Text :
- https://doi.org/10.1111/1759-7714.15216