Radiomics combined with CT features for distinguishing mycoplasma and non-mycoplasma pneumonia in children
10.13929/j.issn.1672-8475.2024.03.006
- VernacularTitle:影像组学联合CT特征鉴别儿童支原体与非支原体肺炎
- Author:
Chao WANG
1
;
Peng XU
;
Guoqiang HUANG
;
Xiaohui QIU
;
Yichao LIU
Author Information
1. 亳州市人民医院影像中心,安徽 亳州 236800
- Keywords:
child;
mycoplasma pneumonia;
tomography,X-ray computed;
radiomics
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(3):155-159
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of radiomics combined with CT features for distinguishing mycoplasma pneumonia(MP)and non-MP in children.Methods Data of 153 children with pneumonia were retrospectively analyzed.The children were divided into MP group(n=101)and non-MP group(n=52)according to mycoplasma RNA detection,and also were divided into training set(n=107,including 71 MP and 36 non-MP)and validation set(n=46,including 30 MP and 16 non-MP)at the ratio of 7∶3.CT findings were compared between groups.Six best CT features were selected in training set using F test algorithm,and a CT model was constructed using logistic regression(LR)method.The best radiomics features were extracted and screened in training set,and machine learning(ML)models were constructed using LR,support vector machine(SVM),random forest(RF),linear discriminant analysis(LDA)and stochastic gradient descent(SGD)classifiers,respectively.Based on the best CT features and radiomics features,CT-ML models were constructed using the above classifiers.Receiver operating characteristic curves were drawn,and the areas under the curve(AUC)were calculated,the efficacy of each model for distinguishing MP and non-MP was evaluated.Results Lesions involved the upper,middle and lower lobe of right lung,thickened bronchial wall,tree bud sign and edge retract sign were the best CT features.AUC of CTLR was 0.710,of MLLR,MLSVM,MLRF,MLLDA and MLSGD in validation set was 0.715,0.663,0.623,0.706 and 0.494,respectively,and MLLR was the optimal radiomics model.AUC of CT-MLLR,CT-MLSVM,CT-MLRF,CT-MLLDA and CT-MLSGD in validation set was 0.813,0.823,0.649,0.796 and 0.665,respectively,and CT-MLSVM was the optimal CT-ML model.In training set,AUC of CT-MLSVM(0.840)was higher than that of CTLR and MLLR model(AUC=0.713,0.740,both P＜0.05).In validation set,no significant difference of AUC was found among CTLR,MLLR and CT-MLSVM(AUC=0.710,0.715 and 0.823,all P＞0.05).Conclusion Radiomics combined with CT features could effectively distinguish MP and non-MP in children.
