ArticleCell reports. Medicine2024
An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.
Article in Cell reports. Medicine, 2024. 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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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
10 citing papers in PubMed.
- Comparison of the normalized cerebral blood volume (CBV) between different models and evaluation of the efficacy of gadolinium leakage in evaluating preoperative adult-type gliomas.Quantitative imaging in medicine and surgery · 2026Article
- Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.Scientific reports · 2025Article
- Real-world radiology data for artificial intelligence-driven cancer support systems and biomarker development.ESMO real world data and digital oncology · 2025Review
- Article
- Comparison of normalized cerebral blood flow between different post-processing methods of dynamic susceptibility contrast perfusion-weighted imaging and arterial spin labeling in gliomas with different grading.Quantitative imaging in medicine and surgery · 2024Article
- Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review.ESMO real world data and digital oncology · 2024Review
- Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.Discover oncology · 2024Article
- DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions.Neuroradiology · 2024Article
- A guide to artificial intelligence for cancer researchers.Nature reviews. Cancer · 2024Review
- Microstructural differentiation of cerebral metastases, glioblastoma, meningioma, and primary CNS lymphoma using advanced diffusion imaging techniques.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.