Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis. (23rd September 2020)

• Record Type: Journal Article
• Authors: Chen, Xin; Zeng, Min; Tong, Yichen; Zhang, Tianjing; Fu, Yan; Li, Haixia; Zhang, Zhongping; Cheng, Zixuan; Xu, Xiangdong; Yang, Ruimeng; Liu, Zaiyi; Wei, Xinhua; Jiang, Xinqing
• Other Names: Zhou Zhiguo Academic Editor.
• Is Part Of: BioMed research international. Volume 2020(2020)
• Publication Date: 2020-09-23
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2020/9258649
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Methylation of the O 6 -methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of MGMT status in 87 glioblastoma patients with contrast-enhanced T1W images and 66 with fluid-attenuated inversion recovery(FLAIR) images. The end-to-end pipeline completes both tumor segmentation and status classification. The better tumor segmentation performance comes from FLAIR images (Dice score, 0.897 ± 0.007 ) compared to contrast-enhanced T1WI (Dice score, 0.828 ± 0.108 ), and the better status prediction is also from the FLAIR images (accuracy, 0.827 ± 0.056 ; recall, 0.852 ± 0.080 ; precision, 0.821 ± 0.022 ; and F 1 score, 0.836 ± 0.072 ). This proposed pipeline not only saves the time in tumor annotation and avoids interrater variability in glioma segmentation but also achieves good prediction of MGMT methylation status. It would help find molecular biomarkers from routine medical images and further facilitate treatment planning.