ArticleJournal of neurosciences in rural practice
Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors.
Article in Journal of neurosciences in rural practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.Scientific reports · 2025Article
- Deep learning-based model for detection of intracranial waveforms with poor brain compliance in southern Thailand.Acute and critical care · 2025Article
- Artificial intelligence and machine learning in healthcare: Scope and opportunities to use ChatGPT.Journal of neurosciences in rural practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors. Materials and Methods: The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL ( Results: The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2 Conclusion: DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.
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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.