Artificial intelligence-assisted CT characterizations and quantitative analysis for differentiating pre-invasive lesions from invasive adenocarcinomas in pulmonary subsolid nodules ≤ 2cm.

Abstract

e21614 Background: The use of artificial intelligence (AI) in medical imaging has dramatically improved the quality of segmentation including accuracy, efficiency and reproducibility. This study sought to determine whether AI-assisted computed tomography (CT) features and quantitative analysis of pulmonary subsolid nodules (SSNs) under 2cm could be used to differentiate preinvasive lesions from invasive adenocarcinomas. Methods: Clinical data and CT images of 297 preinvasive lesions and early invasive lung adenocarcinomas confirmed by surgery pathology with CT manifestations of SSNs under 2cm were retrospectively analysed. The nodules were divided into two groups: the preinvasive lesions (PILs, N = 115) including 7 cases of atypical adenomatous hyperplasia (AAH), 30 cases of adenocarcinoma in situ (AIS) and 78 cases of minimally invasive adenocarcinoma (MIA), and the invasive adenocarcinomas (IACs, N = 182). All CTs were processed by AI and the volume, mean CT value, consolidation-to-tumor ratio (CTR), mass and maximum diameter of each SSN were obtained. Results: The volume, mean CT value, CTR, maximum diameter and mass of nodules showed significant difference between the two groups (Table). Multivariate analysis was determined by logistic regression. The regression model between the two groups was logit(p) = -1.439-2.927Volume +0.0005(mean CT value)-0.463(CTR > 0.5) +0.238(maximum diameter)+6.298(mass).The receiver operating characteristic curve (ROC) showed that the mass can do the best prediction among all the independent factors with the areas under the curve(AUC) 0.748 at a cut-off value of 0.154, with the sensitivity of 70.9% and specificity of 70.4% .The AUC of the ROC using the regression probabilities of regression model was 0.769. Conclusions: AI-assisted CT characterizations may be promising tools to predict if SSNs under 2 cm have invaded. [Table: see text]