ArticleNeuro-oncology advances
Clinical application of machine-based deep learning in patients with radiologically presumed adult-type diffuse glioma grades 2 or 3.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Unravelling differences and hallmarks in suspected diffuse low-grade gliomas: a multicentre database study.Brain communications · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in Methods: Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States. Collected data included patients' age, sex, tumor molecular characteristics ( Results: The mean age in the recruited and external cohorts was 45.0 (SD 14.3) and 44.3 years (SD 14.6). The cohorts were rather similar, except for sex distribution (53.5% vs 64.5% males, Conclusions: In their current state, when these complex models were applied to our clinical scenario, they did not seem to provide a net gain compared to our baseline clinical model.
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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 Socratic graph.