Radiomics Combined with Deep Learning in Predicting Telomerase Reverse Transcriptase Promoter Status in Isocitrate Dehydrogenase-Wildtype Diffuse Astrocytoma
10.3969/j.issn.1005-5185.2024.11.002
- VernacularTitle:影像组学结合深度学习预测异柠檬酸脱氢酶野生型弥漫性星形细胞瘤的端粒酶逆转录酶启动子状态
- Author:
Xinyi XU
1
;
Wang ZHANG
;
Liqiang ZHANG
;
Linling WANG
;
Ming WEN
Author Information
1. 重庆医科大学附属第一医院放射科,重庆 400016
- Keywords:
Astrocytoma;
Magnetic resonance imaging;
Radiomics;
Deep learning;
TERT promoter;
Isocitrate dehydrogenase
- From:
Chinese Journal of Medical Imaging
2024;32(11):1097-1104
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To investigate the fusion model based on MRI radiomics and deep learning to predict the telomerase reverse transcriptase promoter(TERTp)mutation status in isocitrate dehydrogenase-wildtype diffuse astrocytoma.Materials and Methods A retrospective analysis of 175 patients with isocitrate dehydrogenase-wildtype diffuse astrocytoma(122 in the training group and 53 in the test group)from January 2019 to June 2021 in the First Affiliated Hospital of Chongqing Medical University.The Cancer Genome Atlas and The Cancer Imaging Archive were performed to assess TERTp mutation status.The edema and tumor regions were outlined on T1c and T2f images,deep learning model were constructed using the SE-Net model,radiomics features of different regions(edema region,tumor region and overall lesion)were extracted,and 11 features were screened by the least absolute shrinkage and selection operator to build radiomics model.Finally,the radiomics model,deep learning model and clinical model containing Visually Accessible Rembrandt Images features were combined as fusion model,and the model was evaluated using calibration curves and decision curves.Results Six predictive models were eventually built,with an area under curve(AUC)of 0.815(95%CI 0.738-0.892)and 0.645(95%CI 0.494-0.796)for the training and test groups of the clinical model;the AUC for the training and test groups of the deep learning model was 0.860(95%CI 0.798-0.922)and 0.735(95%CI 0.614-0.856);the fusion radiomics model had better predictive performance than the edema or tumor region radiomics models alone,with AUC of 0.906(95%CI 0.856-0.955)and 0.867(95%CI 0.769-0.964)in the training and test groups;the fusion model showed the best performance,with AUC of 0.964(95%CI 0.929-1.000)and 0.905(95%CI 0.818-0.991)in the training and test groups.Conclusion The clinical fusion model of radiomics combined with deep learning performed well in predicting TERTp mutation status in isocitrate dehydrogenase-wildtype diffuse astrocytoma.
