Value of Artificial Intelligence-assisted Chest CT Combined with CT Signs in the Diagnosis of the Benign,Malignant,and Invasive Nature of Pulmo-nary Ground-glass Nodules

WANG Rongping; CHEN Shangyue

doi:10.3969/j.issn.1002-3429.2025.04.008

Abstract：Objective To explore the value of artificial intelligence(AI)-assisted chest CT combined with CT signs in the diagnosis of the benign,malignant,and invasive nature of ground-glass nodules(GGN).Methods A total of 121 pa-tients with pulmonary ground-glass nodules(GGN)from June 2021 to June 2023 were selected and divided into the benign group(n=24)and the malignant group(n=97)based on results of surgical pathology diagnosis.All patients underwent chest CT scans,and the diagnostic value of radiologists'interpretation of CT signs and AI-derived quantitative parameters for the benign and malignant nature of pulmonary GGN was analyzed.The malignant group was further divided into a non-invasive subgroup(n=58)and an invasive subgroup(n=39)according to invasiveness.The AI-derived quantitative parameters of chest CT scans in the two subgroups were compared,and the relationship between each parameter and the invasiveness of ma-lignant pulmonary GGN was analyzed.Results The malignant group had a higher proportion of lobulated sign,spiculated sign,air bronchogram sign,vacuolar sign,pleural indentation sign,and vessel convergence sign than the benign group,and the nodule length,maximum area,volume,average CT value,and cardiothoracic ratio(CTR)were also greater in the malig-nant group than in the benign group(P＜0.05).According to the receiver operating characteristic(ROC)curve analysis,the area under the curve(AUC)of CT signs including lobulated sign,spiculated sign,vacuolar sign,air bronchogram sign,vessel convergence sign,and pleural indentation sign in diagnosing the benign or malignant nature of pulmonary GGN was 0.694,0.663,0.669,0.669,0.642,and 0.756,respectively,with a combined AUC of 0.874.The AUC for AI-derived quantitative parameters including nodule length,maximum area,volume,average CT value,and CTR in diagnosing the be-nign or malignant nature of pulmonary GGN was 0.787,0.792,0.751,0.770,and 0.789,respectively,with a combined AUC of 0.882.The combined diagnosis of CT signs and AI-derived quantitative parameters achieved an AUC of 0.951,indi-cating the optimal diagnostic value.In the infiltrative subgroup,long and short diameters of nodules,maximum area,volume,average CT value,and CTR were greater than those in the non-infiltrative subgroup(P＜0.05).Long and short diameters of nodules,maximum area,volume,average CT value,and CTR were positively correlated with the invasiveness of malignant pulmonary GGN(P＜0.01).Conclusion The combination of AI-assisted chest CT and CT signs diagnosis can effectively improve the diagnostic accuracy of the benign or malignant nature of pulmonary GGN.This combined approach allows for early differentiation between benign and malignant nature of pulmonary GGN,as well as the invasiveness of malignant pulmonary GGN,enabling the development of targeted intervention plans.