Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model
10.12122/j.issn.1673-4254.2024.01.23
- VernacularTitle:基于磁共振图像机器学习放射组学模型预测脑胶质瘤的强化
- Author:
Huishan HE
1
;
Erjia GUO
;
Wenyi MENG
;
Yu WANG
;
Wen WANG
;
Wenle HE
;
Yuankui WU
;
Wei YANG
Author Information
1. 南方医科大学南方医院(第一临床医学院),广东 广州 510515
- Keywords:
brain tumor;
magnetic resonance imaging;
machine learning;
radiomics
- From:
Journal of Southern Medical University
2024;44(1):194-200,封3
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.
