Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction

• Published in: Journal of medical imaging (Bellingham, Wash.) Vol. 9; no. 1; p. 016001
• Main Authors: Nalawade, Sahil S, Yu, Fang F, Bangalore Yogananda, Chandan Ganesh, Murugesan, Gowtham K, Shah, Bhavya R, Pinho, Marco C, Wagner, Benjamin C, Xi, Yin, Mickey, Bruce, Patel, Toral R, Fei, Baowei, Madhuranthakam, Ananth J, Maldjian, Joseph A
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.01.2022
• Subjects: Biomedical Applications in Molecular, Structural, and Functional Imaging; deep learning; magnetic resonance imaging; isocitrate dehydrogenase; motion correction; motion artifact simulation
• ISSN: 2329-4302; 2329-4310
• DOI: 10.1117/1.JMI.9.1.016001

Purpose: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. Approach: T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. Results: Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. Conclusions: Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.