Predictive value of the quantitative model based on artificial intelligence for pathological subtypes of stage Ⅰ invasive lung adenocarcinoma with ground glass nodule
10.3969/j.issn.1002-1671.2023.12.007
- VernacularTitle:基于人工智能定量模型对磨玻璃结节Ⅰ期浸润性肺腺癌病理亚型的预测价值
- Author:
Qi DENG
1
;
Zhifeng XU
;
Dongliang CHENG
;
Tao ZHOU
;
Qinxiang LI
Author Information
1. 佛山市第一人民医院医学影像科,广东 佛山 528000
- Keywords:
artificial intelligence;
ground glass nodule;
lung adenocarcinoma;
pathological subtypes
- From:
Journal of Practical Radiology
2023;39(12):1941-1944,2000
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the predictive value of artificial intelligence(AI)quantitative model for pathological subtypes of stage Ⅰ invasive lung adenocarcinoma with ground glass nodule(GGN).Methods A total of 118 cases(124 lesions)of GGN patients with stage Ⅰ invasive lung adenocarcinoma confirmed by surgery and pathology were analyzed retrospectively,and they were divided into lepidic predominant adenocarcinoma(LPA)group(46 lesions)and non-lepidic predominant adenocarcinoma(n-LPA)group(78 lesions)according to the pathological subtype results.Some relevant AI quantitative parameters were recorded,including the longest diameter,total volume,the percentage of solid volume,total mass,the percentage of solid mass,maximum CT value,minimum CT value,and average CT value.The independent predictors of n-LPA were screened by univariate and multivariate logistic regression analysis,the independent risk factors were quantified by Nomogram,and the diagnostic efficiency of the model was evaluated by using receiver operating characteristic(ROC)curve.Results Binomial logistic regression analysis showed that the percentage of solid mass[odds ratio(OR)=1.965,95％confidence interval(CI)1.515-2.549]and average CT value(OR=1.020,95％CI 1.004-1.036)were independent predictors of n-LPA(P＜0.05).The Nomogram to quantify the independent risk factors showed that the above prediction model was in good agreement with the actual results,and the C-index value was 0.872(95％CI 0.791-0.953).ROC curve analysis showed that the diagnostic performance of the combination of the above two indexes[area under the curve(AUC)=0.829]was better than that of the solid mass percentage(AUC=0.788)and the average CT value(AUC=0.765)of the single indexes,and the corresponding sensitivity and specificity were 87.2％and 84.8％,respectively,which were consistent with the pathological results(Kappa=0.667).Conclusion The percentage of solid mass and the average CT value in the AI quantitative model can effectively help predict the pathological subtypes of GGN stage Ⅰ invasive lung adenocarcinoma,and the combination of the above two indicators can improve the differential diagnosis efficiency of CT between LPA and n-LPA.
