Characterizing the Blood Oxygen Level-Dependent Fluctuations in Musculoskeletal Tumours Using Functional Magnetic Resonance Imaging

Abstract

This study characterized the blood oxygen level-dependent (BOLD) fluctuations in benign and malignant musculoskeletal tumours via power spectrum analyses in pre-established low-frequency bands. BOLD MRI and T1-weighted imaging (T1WI) were collected for 52 patients with musculoskeletal tumours. Three ROIs were drawn on the T1WI image in the tumours’ central regions, peripheral regions and neighbouring tissue. The power spectrum of the BOLD within each ROI was calculated and divided into the following four frequency bands: 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, and 0.198–0.25 Hz. ANOVA was conducted for each frequency band with the following two factors: the location of the region of interest (LoR, three levels: tumour “centre”, “peripheral” and “healthy tissue”) and tumour characteristic (TC, two levels: “malignant” and “benign”). There was a significant main effect of LoR in the frequencies of 0.073–0.198 Hz and 0.198–0.25 Hz. These data were further processed with post-hoc pair-wise comparisons. BOLD fluctuations at 0.073–0.198 Hz were stronger in the peripheral than central regions of the malignant tumours; however, no such difference was observed for the benign tumours. Our findings provide evidence that the BOLD signal fluctuates with spatial heterogeneity in malignant musculoskeletal tumours at the frequency band of 0.073–0.198 Hz.

Comparing the blood oxygen level–dependent fluctuation power of benign and malignant musculoskeletal tumors using functional magnetic resonance imaging

“…The frequency of myogenic activity in the arteriole (0.052-0.15Hz) is similar to the first two frequency points (0.1508 and 0.1534 Hz) related to MSK tumors in current study and the band of 0.073-0.198Hz in the previous study, which may indicate that different fluctuation power of frequency points between the malignant and benign MSK tumors is related to the different power of vasomotion between the malignant and benign MSK tumors (39).…”

“…The spatial and temporal heterogeneity of spontaneous T2* MR signal fluctuations was first observed in an implanted fibrosarcoma mouse model by Baudelet et al (36,37) and later confirmed in tumor xenograft models of colorectal carcinoma by Goncalves and colleagues (38). However, the occurrence of spontaneous BOLD signal fluctuations has not been well demonstrated in human MSK tumors except in our previous published work (39).…”

“…( 36 , 37 ) and later confirmed in tumor xenograft models of colorectal carcinoma by Gonçalves and colleagues ( 38 ). However, the occurrence of spontaneous BOLD signal fluctuations has not been well demonstrated in human MSK tumors except in our previous published work ( 39 ).…”

“…In our previous work, we extracted BOLD signal fluctuations of the 48 MSK tumors by manually selecting the ROIs (regions of interest) in the peripheral and central regions of tumor. The results showed that mean power (the “power” is the square of the BOLD fluctuation amplitude with arbitrary unit) of BOLD fluctuation at a frequency band of 0.073–0.198 Hz was stronger in the peripheral than central regions of the malignant tumors (P < 0.05), whereas no such difference for the benign tumors were there ( 39 ), which may be due to the vasculature difference between the malignant and benign MSK tumors. However, it still did not allow us to differentiate malignant from benign MSK tumors just according to this feature ( 39 ).…”

IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies

“…The ReHo map has been proposed as the alternative to guide seed selection and improve the sensitivity of rs-FC network detection (Yan et al, 2013 ). Besides, local activity could also contain useful information in tumor studies (Duan et al, 2016 ), but we have not yet incorporated this feature in our software. In our Resting-state fMRI module, the preprocessed rs-fMRI data before spatial smoothing was fed into the ReHo analysis for obtaining ReHo map (Zuo et al, 2013 ).…”