The value of predicting the pathological results of labial gland biopsy in Sj?gren's syndrome based on MRI radiomics machine learning models
10.3969/j.issn.1002-1671.2024.10.004
- VernacularTitle:基于MRI影像组学机器学习模型预测干燥综合征唇腺活检病理结果的价值
- Author:
Yunping LIANG
1
;
Hang QU
;
Wei WANG
;
Yue GU
;
Yi ZHOU
;
Yi ZHAO
Author Information
1. 扬州大学附属医院医学影像科,江苏 扬州 225000
- Keywords:
Sj?gren's syndrome;
labial gland;
magnetic resonance imaging;
radiomics
- From:
Journal of Practical Radiology
2024;40(10):1592-1596
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of predicting the pathological results of labial gland biopsy in Sj?gren's syndrome(SS)based on the labial gland MRI radiomics machine learning models.Methods The labial gland MRI data of 178 suspected SS patients were analyzed retrospectively,and the labial gland biopsy pathology results were positive in 97 cases and negative in 81 cases.The samples were divided into training set(143 cases)and test set(35 cases)using a randomized stratified sampling according to the ratio of 4:1.The region of interest(ROI)was manually outlined at the maximal level of the lower labial gland in T2WI water phase and radiomics features(104)were extracted.Feature screening was performed using the least absolute shrinkage and selection operator(LASSO),and the selected features set was used to construct Extra Trees,LightGBM,and Gradient Boosting classifier models.The predictive efficacy of the models was evaluated using the receiver operating characteristic(ROC)curve,and the DeLong test was used to compare the differences in the area under the curve(AUC)between the models.Decision curve analysis(DCA)was used to evaluate the clinical application value of the models in guiding biopsy.Results After LASSO screening,five optimal radiomics features were obtained.The AUC of Extra Trees,LightGBM,and Gradient Boosting models on the training and test sets were as follows 1.000,0.807,0.960 and 0.655,0.779,0.639,respectively.The DeLong test showed no statistically significant difference in AUC among the three models in the test set.DCA showed that the LightGBM model of guided biopsy had a higher clinical net benefit over a wider range of risk thresholds than other models.Conclusion Based on the radiomics features of the labial gland T2WI water phase,the LightGBM model has a high accuracy in predicting the pathological results of labial gland biopsy in SS,and guiding biopsy can obtain high clinical benefits,which has potential clinical application value.
