Open AccessAutomatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis
Author(s) -
Xin Chen,
Min Zeng,
Yichen Tong,
Tianjing Zhang,
Yan Fu,
Haixia Li,
Zhongping Zhang,
Zixuan Cheng,
Xiangdong Xu,
Ruimeng Yang,
Zaiyi Liu,
Xinhua Wei,
Xinqing Jiang
Publication year - 2020
Publication title -
biomed research international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 126
eISSN - 2314-6141
pISSN - 2314-6133
DOI - 10.1155/2020/9258649
Subject(s) - algorithm , artificial intelligence , computer science , machine learning
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.