Abstract
Deep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization.
STATEMENT OF SIGNIFICANCE Deep learning networks have shown promise for predicting molecular profiles of gliomas using MR images. We showed that patient motion artifact, which is frequently encountered in the clinic, can significantly impair the performance of these algorithms. Robust motion correction algorithms can restore the performance of these networks rivaling tissue-based characterization.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Financial Support: This work was supported by NIH/NCI U01CA207091 (AJM, JAM).