Value of artificial intelligence analysis of CT quantitative parameters of ground-glass nodules in predicting subtypes of lung adenocarcinoma
YANG Jianli1,2, NIU Haiya2, YU Jing2, HAN Wenqian2, QI Maliya2, MU Xuetao3
1. Department of Radiology, Training Site for Postgraduate of Jinzhou Medical University, Jinzhou 121000,China; 2. Department of Radiology, Changji Branch of the First Affiliated Hospital of Xinjiang Medical University, Changji 831100, China; 3. Department of Radiology, the Third Medical Center of Chinese PLA General Hospital, Beijing 100039,China
Abstract：Objective To explore the predictive value of the CT quantitative parameters of ground-glass nodules (GGN) by artificial intelligence (AI) assisted diagnosis system for lung adenocarcinoma subtypes.Methods A total of 97 cases of GGN confirmed by surgery and pathology were retrospectively analyzed, and they were divided into non-invasive group and invasive group according to the degree of infiltration of the lesions. The characteristics of AI quantitative parameters were extracted, and the independent sample T test was used to compare the statistical differences between the two groups. Receiver operator characteristic curve (ROC) and binary logistic regression model were used to predict the degree of invasion of GGN lesions to evaluate the diagnostic performance of AI quantitative parameters.Results The maximum diameter GGN, volume, average CT value and proportion of solid components in the non-invasive group and the invasive group were different between the two groups (P<0.05). The prediction value of CT quantitative parameters from high to low was: solid component proportion, average CT value, maximum diameter, and volume. Logistic regression analysis showed that the proportion of real components (OR=1.262, P<0.05) and mean CT value (OR=1.010, P<0.05) had high diagnostic value in predicting GGN invasion and could be used as independent predictors, diagnostic thresholds 1.085% and -557.00 HU.Conclusions AI can effectively predict lung adenocarcinoma subtypes by analyzing the proportion of solid components and average CT value of GGN.
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2022, Vol. 33 
