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Sci Rep. 2018 Jan 19;8(1):1223. doi: 10.1038/s41598-017-18453-0.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis.

Huang H1,2,3, Lu J4, Wu J4, Ding Z5, Chen S6, Duan L7, Cui J7, Chen F8, Kang D8, Qi L9, Qiu W10, Lee SW11, Qiu S12, Shen D13,14, Zang YF1,3, Zhang H15,16,17.

Author information

1
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China.
2
School of Psychology, South China Normal University, Guangzhou, 510631, China.
3
Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, China.
4
Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China.
5
Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, China.
6
Department of Neurosurgery, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310014, China.
7
Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, China.
8
Department of Neurosurgery, No.1 Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China.
9
Department of Radiology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, China.
10
Department of Neurosurgery, Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, 310015, China.
11
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
12
Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
13
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. dgshen@med.unc.edu.
14
Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea. dgshen@med.unc.edu.
15
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China. hanzhang@med.unc.edu.
16
Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, 310015, China. hanzhang@med.unc.edu.
17
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. hanzhang@med.unc.edu.

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.

PMID:
29352123
PMCID:
PMC5775317
DOI:
10.1038/s41598-017-18453-0
[Indexed for MEDLINE]
Free PMC Article

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