ArticleNeuroImage. Clinical2019
Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.Neuroradiology · 2026Article
- Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).Quantitative imaging in medicine and surgery · 2024Article
- Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.The British journal of radiology · 2023Article
- Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.European radiology · 2023Article
- Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.Cancers · 2022Review
- The progress of multimodal imaging combination and subregion based radiomics research of cancers.International journal of biological sciences · 2022Review
- Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.Scientific reports · 2021Article
- Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors.European journal of nuclear medicine and molecular imaging · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.
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