Deep learning combine with radiomics based on MRI for evaluating H3 K27 status of midline gliomas
10.13929/j.issn.1003-3289.2024.06.004
- VernacularTitle:基于MRI的深度学习联合影像组学评估中线胶质瘤H3 K27状态
- Author:
Jiaqi TU
1
;
Zhongxiang LUO
;
Jianpeng LIU
;
Haoqing CHEN
;
Bo JIN
;
Fengping ZHU
;
Yuxin LI
;
Bin HU
Author Information
1. 复旦大学附属华山医院放射科,上海 200040
- Keywords:
glioma;
deep learning;
magnetic resonance imaging;
radiomics
- From:
Chinese Journal of Medical Imaging Technology
2024;40(6):810-814
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of deep learning combine with radiomics based on MRI for evaluating H3 K27 status of midline gliomas.Methods Totally 127 patients with diffuse midline glioma H3 K27-altered(H3-DMG)and 127 patients with midline glioblastoma(GBM)without H3 K27 mutation were retrospectively enrolled.The patients were randomly divided into training set(n=204)and test set(n=50)at the ratio of 8:2.U-Net neural network visual and radiomics features of tumors were extracted based on MRI,and a deep learning radiomics model was established,its value for evaluating H3 K27 status was observed.Results Based on training set,0.500 was obtained as the security score partition value for the model classification task.In test set,the median safety score of the obtained deep learning radiomics model for evaluating H3 K27 status of H3-DMG and GBM was 0(0,0)and 0.999(0.616,1.000),respectively,for the former was lower than for the latter(Z=-5.114,P＜0.001).The sensitivity,specificity,accuracy and area under the curve of deep learning radiomics model for evaluating H3 K27 status in training set was 93.14％,81.37％,87.25％and 0.953(95％CI[0.923,0.976]),respectively,while was 88.00％,80.00％,84.00％and 0.922(95％CI[0.829,0.986])in test set,respectively.Conclusion Deep learning radiomics based on MRI could accurately evaluate H3 K27 status of midline gliomas.
