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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number016001
JournalJournal of Medical Imaging
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2022

Keywords

  • deep learning
  • isocitrate dehydrogenase
  • magnetic resonance imaging
  • motion artifact simulation
  • motion correction

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this