Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Hamed Akbari,Christos Davatzikos,Arati S Desai,Elizabeth Mamourian,Ali Nabavizadeh,Donald M O’Rourke,Jeffrey B Ware,Jingwen Yao,Stephen J Bagley,Anahita Fathi Kazerooni,Catalina Raymond,Samantha Guiry,Hannah Anderson,Chiharu Sako,Steven Brem,Benjamin M Ellingson

doi:10.1038/s41598-021-94560-3

Abstract

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman’s r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Highlights

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults, characterized with vasular proliferation, diffuse infiltration in the adjacent brain parenchyma, and resistance to the standard therapies[1]
The support vector machines regression (SVR) machine learning method based on principal component analysis (PCA) was applied in a cohort of 78 cases
Relative cerebral blood volume map generated from Dynamic susceptibility contrast (DSC)-MRI scans using Cancer Imaging Phenomics Toolkit (CaPTk) software, PC1–PC3 images derived from principal components (PCs) analysis of the hemodynamic curves, the MTRasym image constructed using our proposed approach, along with the actual MTRasym image quantified from Chemical exchange saturation transfer (CEST) imaging are shown in the whole pathogenic region

Summary

Introduction

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults, characterized with vasular proliferation, diffuse infiltration in the adjacent brain parenchyma, and resistance to the standard therapies[1]. Neo-angiogenesis forms a tortuous and branched vascular structure with increased blood volume and permeability, and impaired cerebral perfusion with subsequent necrosis[3,5] These alterations promote tumor growth, decrease oxygen, increase glycolysis and lactic acid, decrease extracellular pH, facilitate cell invasion[6]. PC analysis of DSCMRI has shown potential in predicting the location of future recurrence[16,17], patient’s survival 18, arteriovenous shunting[4], and EGFRvIII status[19] The aim of this analysis is to use ML methods based on perfusion MRI scans to uncover unique tissue characteristics that correlate with tissue acidity and might provide insights about the tumoral and peritumoral tissue metabolism to guide treatment planning

Methods

Results

Conclusion