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- Title
MRI-Based Deep Learning Method for Classification of IDH Mutation Status.
- Authors
Bangalore Yogananda, Chandan Ganesh; Wagner, Benjamin C.; Truong, Nghi C. D.; Holcomb, James M.; Reddy, Divya D.; Saadat, Niloufar; Hatanpaa, Kimmo J.; Patel, Toral R.; Fei, Baowei; Lee, Matthew D.; Jain, Rajan; Bruce, Richard J.; Pinho, Marco C.; Madhuranthakam, Ananth J.; Maldjian, Joseph A.
- Abstract
Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin–Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.
- Subjects
DEEP learning; UNIVERSITY of Wisconsin (Madison, Wis.); NEW York University; UNIVERSITY of California, San Francisco; MACHINE learning; RECEIVER operating characteristic curves; DATABASES; ISOCITRATE dehydrogenase; BRAIN tumors
- Publication
Bioengineering (Basel), 2023, Vol 10, Issue 9, p1045
- ISSN
2306-5354
- Publication type
Article
- DOI
10.3390/bioengineering10091045