ArticleScientific reports2018
Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 33 citations in OpenAlex.
- The Evolving Landscape of Ovarian Cancer: Innovations in Biotechnology and Artificial Intelligence- Based Screening and Treatment.Current treatment options in oncology · 2025Review
- Improved brain tumor diagnostics and follow-up with novel magnetic resonance imaging methods: A single center study protocol.PloS one · 2025Article
- Clustering Functional Magnetic Resonance Imaging Time Series in Glioblastoma Characterization: A Review of the Evolution, Applications, and Potentials.Brain sciences · 2024Review
- Personalized fMRI Delineates Functional Regions Preserved within Brain Tumors.Annals of neurology · 2022Article
- Comparing the blood oxygen level-dependent fluctuation power of benign and malignant musculoskeletal tumors using functional magnetic resonance imaging.Frontiers in oncology · 2022Article
- BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients' Recovery.Cancers · 2021Article
- Multivariate machine learning-based language mapping in glioma patients based on lesion topography.Brain imaging and behavior · 2021Article
- Pilot Clinical Study Investigating the Thermal Physiology of Breast Cancer via High-Resolution Infrared Imaging.Bioengineering (Basel, Switzerland) · 2021Article
- A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences.Computational and mathematical methods in medicine · 2020Article
- Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 3 countries.
Funding
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.
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