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
identifcation. 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 diferent total number of components (TNCs). The best-ftted tumorrelated 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 fndings
suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment