Diagnostic values of radiomics models in micro-calcifications in carotid plaques
10.3760/cma.j.cn115354-20221030-00766
- VernacularTitle:影像组学模型对颈动脉斑块内微钙化的诊断价值研究
- Author:
Xin CHEN
1
;
Hao ZHANG
;
Ying HE
;
Song YANG
;
Liping CAO
;
Mengmeng WANG
;
Yazhou MA
;
Fei HUA
;
Xuegan LIAN
Author Information
1. 苏州大学附属第三医院神经内科，常州 213003
- Keywords:
Carotid;
Plaque;
Micro-calcification;
Ultrasound;
Radiomics;
Judging model
- From:
Chinese Journal of Neuromedicine
2023;22(6):547-552
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct radiomics models of micro-calcification in carotid plaques, and compare their diagnostic values.Methods:Fifty-two patients with large atherosclerotic cerebral infarction admitted to Department of Neurology, Third Affiliated Hospital of Soochow University from May 2017 to November 2019 were enrolled. All patients underwent conventional carotid artery Doppler ultrasound to detect carotid plaques and Micropure? ultrasound to detect micro-calcifications in the plaques. A cross-section image with maximum numbers of micro-calcifications was chosen when there were micro-calcifications in carotid plaques; otherwise, a cross-section image with the largest area of the plaque was chosen. After all images were normalized by Photoshop software, the plaques were delineated as regions of interest using MaZda 4.6 software and 283 texture features of the plaques were automatically extracted. The texture features with the strongest predictive value were selected through consistency analysis (intrclass correlation coefficient [ICC]>0.75), two-sample t-test, Least absolute shrinkage and selection operator (Lasso) regression. The predictive models were constructed by RandomForest (RF) and Support vector machine (SVM) classifiers. The training set and test set were divided by 7: 3 to analyze the classification accuracy. Receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC) to evaluate the diagnostic values of the models. Delong test was used to compare the difference between the diagnostic values of the 2 classifiers in test set. Results:A total of 148 plaque images from 52 patients were enrolled, including 104 plaques with micro-calcification and 44 plaques without micro-calcification. Nine texture features were finally selected after ICC analysis, T test and Lasso regression: 5 image gray histogram features were mean, variance, skewness, kurtosis and 99 th percentile (Perc. 99%); 1 autoregressive model feature was Teta3, and 3 wavelet transform features were WavEnLH_s-3, WavEnLH_s-4, and WavEnLH_s-6. With RF classifier, accuracy of the diagnostic model was 0.93, enjoying AUC of 0.92; with SVM classifier, that was 0.91, enjoying AUC of 0.90; Delong test showed that the diagnostic values of the 2 classifiers in test set were significantly different ( Z=1.000, P=0.320). Conclusion:Radiomic models constructed by RF and SVM classifiers can identify micro-calcification in carotid plaques, and the 2 classifiers share equivalent diagnostic values.
