MRI-Based Deep Learning Method for Classification of IDH Mutation Status.

Autor: Bangalore Yogananda CG; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Wagner BC; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Truong NCD; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Holcomb JM; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Reddy DD; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Saadat N; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Hatanpaa KJ; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Patel TR; Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Fei B; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.; Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA., Lee MD; Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA., Jain R; Department of Radiology, NYU Grossman School of Medicine, New York, NY 10016, USA.; Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY 10016, USA., Bruce RJ; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA., Pinho MC; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Madhuranthakam AJ; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA., Maldjian JA; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Jazyk: angličtina
Zdroj: Bioengineering (Basel, Switzerland) [Bioengineering (Basel)] 2023 Sep 05; Vol. 10 (9). Date of Electronic Publication: 2023 Sep 05.
DOI: 10.3390/bioengineering10091045
Abstrakt: 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.
Databáze: MEDLINE
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