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Abstract #1693

Radiomics features from APTw MRI improve the diagnostic performance of structural MRI in early response assessment of malignant gliomas

Shanshan Jiang1, Pengfei Guo2, Hye-Young Heo1, John Laterra3, Charles Eberhart4, Michael Lim5, Peter C.M. van Zijl1,6, and Jinyuan Zhou1
1Radiology, Johns Hopkins University, Baltimore, MD, United States, 2Computer Science, Johns Hopkins University, Baltimore, MD, United States, 3Neurology, Johns Hopkins University, Baltimore, MD, United States, 4Pathology, Johns Hopkins University, Baltimore, MD, United States, 5Neurosurgery, Johns Hopkins University, Baltimore, MD, United States, 6F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States

Assessment of glioma treatment is based on pathological evaluation via biopsies or radiological criteria using follow-up MRI, which is either invasive or time consuming. Amide protein transfer weighted (APTw) MRI has been validated to accurately detect recurrent malignant gliomas by multiple studies. The cutting edge methodology of radiomics provides quantitative measurements for imaging diagnosis. Here, we develop an automated framework that integrates APTw MRI radiomic features with a machine learning model to evaluate treatment response for gliomas. Our results suggest that the use of APTw features enabled the radiomic model to reach a more accurate assessment of the treatment effect.

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