Objective:To explore the predictive value of quantitative parameters of artificial intelligence(AI)in the pathological types of ground glass nodule(GGN)of lung.Methods:Sixty-five patients with lung GGN confirmed by surgery and pathology from January 2022 to April 2023 were retrospectively collected and divided into three groups according to the new classification standard of lung tumors of WHO in 2021:precursor gland lesion group(atypical adenomatous hyperplasia AAH+carcinoma in situ AIS group),micro-invasive adenocarcinoma group(MIA group)and invasive adenocarcinoma group(IAC group);In addition,they were divided into two groups according to whether they were invasive:non-invasive adenocarcinoma group(AAH+AIS group)and invasive adenocarcinoma group(MIA+IAC group);All patients underwent chest CT scanning and thin-layer reconstruction of lung window,and seven related quantitative parameters of GGN were measured by AI method:long diameter(mm),short diameter(mm),volume(mm3),average CT value(Hu),maximum CT value(Hu),minimum CT value(Hu)and solid ratio(％).The differences of AI quantitative parameters among different groups were statistically analyzed,and the effectiveness of parameters in predicting pathological types of lung GGN was evaluated by ROC curve.Results:There were 22 cases of precursor gland lesions,21 cases of micro-invasive adenocarcinoma and 22 cases of invasive adenocarcinoma.Seven quantitative parameters of AI were statistically different among the three groups(P＜0.05),and the values of seven parameters showed an increasing trend from precursor gland lesions to invasive adenocarcinoma.Compared with the invasive adenocarcinoma group,the long diameter,short diameter,volume and maximum CT value showed statistical differences(P＜0.05),while the average CT value,minimum CT value and solid ratio were not statistically significant(P＞0.05).According to the analysis of ROC curve,the cutoff value of length and diameter of GGN is 8.5mm,and the area under curve(AUC)is 0.758,with sensitivity of 67.4％and specificity of 68.2％.The critical value of short diameter is 6.5mm,AUC=0.736,sensitivity is 69.8％,and specificity is 59.1％.The volume boundary value was 194.47mm3,AUC=0.734,sensitivity was 76.7％,and specificity was 59.1％.The maximum value of CT was-138.5HU,AUC=0.723,sensitivity was 79.1％,and specificity was 63.6％.Conclusion:Al quantitative parameters can effectively predict the pathological types of lung GGN before operation,especially the length and diameter of GGN have the greatest prediction efficiency.With the increase of the length and diameter,the chances for invasive adenocarcinoma are greater,which provides important reference for timely clinical intervention and prognosis evaluation,and is worthy of wide application.
维普
万方数据