Abstract #0148

Improved diagnostic performance of APTw MRI to multiparametric non-contrast-enhanced MRIs in patients with post-treatment high-grade gliomas

Qianqi Huang^1,2, Jingpu Wu^1,3, Yiqing Shen^1,2, Nhat Le^1,2, Pengfei Guo^1,2, Karisa Schreck⁴, David Kamson⁴, Lindsay Blair⁴, Hye Young Heo¹, Xu Li^1,5, Wenbo Li^1,5, Haris Sair¹, Jaishri Blakeley⁴, John Laterra⁴, Matthias Holdhoff⁶, Stuart Grossman⁶, Debraj Mukherjee⁷, Chetan Bettegowda ⁷, Peter van Zijl ^1,5, Jinyuan Zhou¹, and Shanshan Jiang¹

¹Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ²Department of Computer science, Johns Hopkins Univeristy, Baltimore, MD, United States, ³Department of Applied Mathematics and Statistics, Johns Hopkins Univeristy, Baltimore, MD, United States, ⁴Department of Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁵Department of Radiology, Kennedy Krieger Institute, Baltimore, MD, United States, ⁶Department of Oncology, Johns Hopkins University, Baltimore, MD, United States, ⁷Department of Neurosurgery, Johns Hopkins University, Baltimore, MD, United States

Synopsis

Keywords: Tumors, TreatmentWe explored non-contrast-enhanced MRI performances of APTw, DWI, SWI, and pCASL at 3 Tesla in glioma patients post-treatment. APTw, ADC, QSM, and CBF histogram parameters from volumetric ROIs were recorded. Multivariable logistics regression with principal component analysis (PCA) was built for differentiating treatment effect from tumor recurrence. Results showed that the regression model trained on the combination of APTw, CBF, and QSM achieved the highest classification performance, with an AUC of 0.90.

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