Abstract #1963

Histogram model of DCE-MRI in predicting progression of enhancing non-measurable disease after chemoradiotherapy in high-grade glioma

Haimei Cao¹, Zhousan Huang¹, Ruowei Qiu¹, Zhiyong Li², Kan Deng³, Jay J Pillai⁴, Guanglong Huang², Yikai Xu¹, Jun Hua^5,6, and Yuankui Wu¹

¹Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China, ²Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China, ³Philips Healthcare, Guangzhou, China, ⁴Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵Neurosection, Division of MRI Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁶F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Keywords: Tumors, Perfusion, enhancing non-measurable disease (NMD)Early prediction of disease progression is of potential clinical significance for the management of high-grade glioma (HGG) patients. We investigated the value of histogram models based on volume transfer constant (K^trans) between the plasma and extravascular extracellular space and extravascular volume (Ve) in predicting the progression of enhancing non-measurable diseases (NMD) of HGG after chemoradiotherapy. Our results showed that histogram models based on K^trans and Ve can accurately predict the progression of enhancing NMD of HGG following chemoradiotherapy, and combining K^trans and Ve helps improve the prediction performance.

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