Abstract
Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.
Highlights
Inversion recovery (FLAIR) MRI, have provided the gold standard for delineating brain tumors from various view angles according to their respective imaging characteristics[7,9]
This study demonstrates that brain tumor may have unique Blood oxygen level-dependent (BOLD) functional MRI (fMRI) signals compared with those of other normal brain tissues
The initial threshold (z > 1 and cluster size > 20 voxels) were empirically selected based on our experience and other previous studies, but this is not a strict parameter since we found that the tumor detection result was not changed using other thresholds ranging from z > 0.5 to z > 3
Summary
Inversion recovery (FLAIR) MRI, have provided the gold standard for delineating brain tumors from various view angles according to their respective imaging characteristics[7,9]. Compared to the conventional contrast-enhanced imaging, BOLD fMRI is non-invasive, free-of-contrast-agent, and sensitive to abnormal vascularization and perfusion caused by the tumor It provides functional information for time series analysis, allowing more data mining options. We hypothesize that brain tumor has distinct BOLD signals compared to other surrounding normal tissues, and that ICA, as a sensitive blind source separation method, may be able to identify such a “tumor-related” component due to the distinct spatiotemporal BOLD characteristics in the tumor region. We developed a largely automated tumor-related component identification algorithm with only minimal human intervention to prevent bias or subjectivity in the conventional manual tumor labeling This method may serve as a novel, non-invasive, free-of-contrast-agent technique for tumor tissue functional delineation towards comprehensive presurgical assessment. These potentialities could make this new technique broadly interested and widely applicable in future clinical practice
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