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Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules
- Source :
- Annals of Medicine, Vol 56, Iss 1 (2024)
- Publication Year :
- 2024
- Publisher :
- Taylor & Francis Group, 2024.
-
Abstract
- Introduction Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction.Methods Patients with stage 0–IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN.Results Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules.Conclusions Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.
- Subjects :
- Artificial intelligence
prediction
lung cancer
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 07853890 and 13652060
- Volume :
- 56
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Annals of Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.318848f8b9dd48afa90a52abb24c4aa6
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/07853890.2024.2405075