Evidence map›Paper›PMID 36959422›Full record

ArticleNature medicine2023

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Todd Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher and 15 more

Open access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 3 pooled it
56.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

97 citing papers in PubMed, 3 syntheses or guidelines pooled it, 158 citations in OpenAlex.

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  16. Qualitative and quantitative assessment ofBiomedical optics express · 2026
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37 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

25 authors at 6 institutions in 3 countries.

Todd HollonMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA. tocho@med.umich.edu.ORCID http://orcid.org/0000-0001-5987-6531
Cheng JiangMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0003-1759-4960
Asadur ChowduryMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-5653-9721
Mustafa Nasir-MoinDepartment of Neurosurgery, New York University, New York, NY, USA.
Akhil KondepudiMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Alexander AabediDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-7935-5458
Arjun AdapaMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Wajd Al-HolouDepartment of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Jason HethDepartment of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Oren SagherDepartment of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
Pedro LowensteinDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Maria CastroDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Lisa Irina WadiuraDepartment of Neurosurgery, Medical University Vienna, Vienna, Austria.ORCID http://orcid.org/0000-0002-0749-7684
Georg WidhalmDepartment of Neurosurgery, Medical University Vienna, Vienna, Austria.ORCID http://orcid.org/0000-0001-6014-0273
Volker NeuschmeltingDepartment of Neurosurgery, University Hospital Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0001-7527-6990
David ReineckeDepartment of Neurosurgery, University Hospital Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-3298-9517
Niklas von SpreckelsenDepartment of Neurosurgery, University Hospital Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-9873-1711
Mitchel S BergerDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Shawn L Hervey-JumperDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
John G GolfinosDepartment of Neurosurgery, New York University, New York, NY, USA.
Matija SnuderlDepartment of Pathology, New York University, New York, NY, USA.
Sandra Camelo-PiraguaDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Christian FreudigerInvenio Imaging, Inc., Santa Clara, CA, USA.
Honglak LeeElectrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Daniel A OrringerDepartment of Neurosurgery, New York University, New York, NY, USA.ORCID http://orcid.org/0000-0001-6427-8376
University of Michigan · USUniversity of California, San Francisco · USNew York University · USUniversity Hospital Cologne · DEMedical University of Vienna · ATInvenio Imaging (United States) · US

Funding

Transitioning Early Career Neurosurgeons to Scientific IndependenceK12NS080223 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ESKANDAR, EMAD N · 2012 to 2022
$9.2M
Clinical Translation of Stimulated Raman HistologyR01CA226527 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ORRINGER, DANIEL · 2018 to 2025
$4.4M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
NCI NIH HHS R01 CA226527NIGMS NIH HHS T32 GM141746NINDS NIH HHS K12 NS080223
6 · The paper itself

Abstract

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n = 153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.3 ± 1.6%. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

Indexed as

Brain NeoplasmsGliomaAdultArtificial IntelligenceHumansIntelligenceIsocitrate DehydrogenaseMutationOptical ImagingProspective StudiesIsocitrate Dehydrogenase

Identifiers

PMID36959422
PMCPMC10445531
OpenAlexW4360610013

What OpenQuestion holds

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Registered trials

None linked

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.