Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules
Year: 2024
Type: article
Study design: Observational Study
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.
Source: Annals of Medicine
Authors: Long Jiang, Yang Zhou, Wang Miao, Hongda Zhu, Ningyuan Zou, Yu Tian, Hanbo Pan, Weiqiu Jin, Jia Huang, Qingquan Luo
Institutions: Shanghai Jiao Tong University, Shanghai Chest Hospital
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,557
OpenAlex ID: W4402646954