Tumor tissue detection using blood-oxygen-level-dependent functional MRI based on independent component analysis

Huiyuan Huang, Junfeng Lu, Jinsong Wu, Zhongxiang Ding, Shuda Chen, Lisha Duan, Jianling Cui, Fuyong Chen, Dezhi Kang, Le Qi, Wusi Qiu, Seong Whan Lee, Shi Jun Qiu, Dinggang Shen, Yu Feng Zang, Han Zhang

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25 Citations (Scopus)


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.

Original languageEnglish
Article number1223
JournalScientific reports
Issue number1
Publication statusPublished - 2018 Dec 1

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© 2018 The Author(s).

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