Abstract #4459

Predicting tumor recurrence in patients with gliomas via deep learning-based analysis of structural and amide proton transfer weighted MRI

Pengfei Guo^1,2, Mathias Unberath², Jinyuan Zhou¹, Hye-Young Heo¹, Charles G. Eberhart³, Michael Lim⁴, Jaishri O. Blakeley⁵, Peter van Zijl^1,6, and Shanshan Jiang¹

¹Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States, ³Department of Pathology, Johns Hopkins University, Baltimore, MD, United States, ⁴Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, United States, ⁵Department of Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁶F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Amide protein transfer weighted (APTw) MRI has been validated to accurately detect recurrent malignant gliomas across different studies. However, APTw image interpretation is time consuming and requires professional knowledge. Therefore, reliable, automated imaging diagnostic tools to assess malignant glioma response to therapies are urgently needed. Here, we develop and verify a CNN-based deep-learning algorithm to identify tumor progression versus response by adding APTw MRI data to structural MR images as the proposed model input. Our results suggest that the use of APTw images can increase the diagnostic accuracy to structural MRI for the treatment response assessment.

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