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Abstract #2925

Probing tumor microenvironment with time-dependent diffusion MRS and machine learning based modeling in C6 Glioma

Ke Zhou1, Ziyan Wang2, Yang Cao3, Xinyi Zhu1, Yufan Zhou1, Yibo Liu4, Chunli Cai5, Yi-Cheng Hsu6, Sheng Chen4, and Min Wang1,7
1Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, 2Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 3Beijing Wandong Medical Technology Co.,Ltd., Beijing, China, 4Department of Neurosurgery, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China, 5Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China, 6MR Collaboration, Siemens Healthcare Ltd, Shanghai, China, 7School of Medicine, Sir Run Run Shaw Hospital, Department of Endocrinology, Zhejiang University, Hangzhou, China

Synopsis

Keywords: Spectroscopy, Microstructure

Motivation: Diffusion-weighted MR Spectroscopy(DW-MRS) enables access to diffusion properties of intracellular metabolites to characterize brain cell-specific microstructure, potentially reflecting cellular changes in tumor microenvironment(TME).

Goal(s): To measure time-dependent diffusion kurtosis of intracellular metabolites and to compute biophysical model of cellular changes in TME.

Approach: In-vivo DW-MRS and machine learning(ML)-based model were applied to measure tumor microstructural changes with different diffusion-times(Td) in 11 normal and 11 C6-glioma rat models.

Results: Changes in neuronal(tNAA) and glial(tCho) metabolites' time-dependent diffusion indicate neuronal atrophy and glial activation within tumor core. Microstructural and metabolic alterations, including specific cell size changes during tumor growth, suggest increased invasiveness in malignant tumors.

Impact: This work provides a unique insight into the Td-dependency of intracellular metabolites and water to probe the microstructural changes during the pathological tumor progression, which helps revealing spatial structural and metabolite profile to address the complexity of the TME.

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Keywords