BOLD asynchrony elucidates tumor burden in IDH-mutated gliomas

Abstract

Background: Gliomas comprise the most common type of primary brain tumor, are highly invasive, and often fatal. IDH-mutated gliomas are particularly challenging to image and there is currently no clinically accepted method for identifying the extent of tumor burden in these neoplasms. This uncertainty poses a challenge to clinicians who must balance the need to treat the tumor while sparing healthy brain from iatrogenic damage. The purpose of this study was to investigate the feasibility of using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to detect glioma-related asynchrony in vascular dynamics for distinguishing tumor from healthy brain.

Methods: Twenty-four stereotactically localized biopsies were obtained during open surgical resection from ten treatment-naïve patients with IDH-mutated gliomas who received standard-of-care preoperative imaging as well as echo-planar resting-state BOLD fMRI. Signal intensity for BOLD asynchrony and standard-of-care imaging was compared to cell counts of total cellularity (H&E), tumor density (IDH1 & Sox2), cellular proliferation (Ki67), and neuronal density (NeuN), for each corresponding sample.

Results: BOLD asynchrony was directly related to total cellularity (H&E, P = 4 × 10-5), tumor density (IDH1, P = 4 × 10-5; Sox2, P = 3 × 10-5), cellular proliferation (Ki67, P = .002), and inversely related to neuronal density (NeuN, P = 1 × 10-4).

Conclusions: Asynchrony in vascular dynamics, as measured by resting-state BOLD fMRI, correlates with tumor burden and provides a radiographic delineation of tumor boundaries in IDH-mutated gliomas.

Keywords: BOLD asynchrony; IDH-mutated glioma; infiltration; resting-state fMRI; tumor burden.

Fig. 1

Pre-operative scans of pathology-proven IDH-mutated grade II (A) and grade III (B) gliomas displaying both structural MRI (T1 post-contrast, T2, FLAIR, and ADC) in the first four columns and functional MRI (rCBV and BA) in the last two columns. There were no obvious rCBV abnormalities near the tumor. In comparison, well-demarcated areas of vascular dysregulation were captured using BA.

Fig. 2

(A) Two representative biopsies are displayed from radiographically distinct areas with low BA (Z = –3) in the top row and high BA (Z = 10.5) in the bottom row. The top row depicts low cellularity (H&E), low tumor density (IDH1 and Sox2), low cellular proliferation (Ki67), and high neuronal density (NeuN) whereas the bottom row depicts high cellularity, high tumor density, high cellular proliferation, and low neuronal density with the same markers. (B) Red lines represent significant regression fits between histologic and radiographic data. BA and ADC were significantly predictive of all histological features. In contrast, rCBV and FLAIR showed no significant relationship to histology.

Fig. 3

A representative biopsy acquired from radiographically normal brain on standard-of-care imaging but with high BA and tumor burden as measured by IDH1 and Sox2.

Fig. 4

Tumor infiltration decreases exponentially as a function of distance (A) and can be used in conjunction with BA to generate a predictive model of tumor burden (B) that extends beyond what is visible on standard-of-care imaging.