ArticleScientific reports2020
Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.AJNR. American journal of neuroradiology · 2022Pooled it
- Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- DSC Perfusion MRI Artefact Reduction Strategies: A Short Overview for Clinicians and Scientific Applications.Journal of clinical medicine · 2025Review
- Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.Discover oncology · 2024Article
- An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.Cell reports. Medicine · 2024Article
- Deep learning aided preoperative diagnosis of primary central nervous system lymphoma.iScience · 2024Article
- Article
- Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.AJNR. American journal of neuroradiology · 2023Article
- Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review).Oncology reports · 2023Article
- MUC21 induces the viability and migration of glioblastoma via the STAT3/AKT pathway.Experimental and therapeutic medicine · 2022Article
- Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.Cancers · 2022Review
- Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.
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