Evidence map›Paper›PMID 42789696›Full record

ArticleScience advances2026

Optical microscopy predictions of focal recurrence in glioblastoma.

Sanjeev Herr, Niels Olshausen, Melike Pekmezci, Jasleen Kaur, Youssef Sibih, Vardhaan Ambati, Abraham Dada, Katie Scotford, Amit Persad, Thiebaud Picart and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

26 authors.

Sanjeev HerrDrexel University College of Medicine, Philadelphia, PA 19129, USA.ORCID 0000-0002-4302-2854
Niels OlshausenDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0009-0003-3698-454X
Melike PekmezciDepartment of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0003-2548-8359
Jasleen KaurDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Youssef SibihDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0002-0839-5428
Vardhaan AmbatiDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0003-0074-9217
Abraham DadaDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0002-2125-7011
Katie ScotfordDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Amit PersadDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Thiebaud PicartDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0001-6494-6725
Akhil KondepudiMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48105, USA.
Gabrielle MalteDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Gabriella VulakhDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Johanna PechmannDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Jessica MakolliDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Nancy Ann Oberheim-BushDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.
Albert KimDepartment of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA.ORCID 0000-0002-1751-8493
Jacob YoungDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0002-5499-4325
Mitchel S BergerDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0003-1983-4892
Ammar MallouhiDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.ORCID 0000-0003-2232-7330
Barbara KieselDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0003-0939-9831
Georg WidhalmDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0001-6014-0273
Lisa KörnerDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.ORCID 0000-0002-9227-461X
Madhumita SushilDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0001-7884-0526
Todd HollonMachine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48105, USA.ORCID 0000-0001-5987-6531
Shawn L Hervey-JumperDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA.ORCID 0000-0003-4699-260X

Funding

Project 4: Multi-modality Metabolic Imaging for Monitoring Molecular Sub-types of GliomaP01CA118816 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Susan M Chang, Janine Marie Lupo · 2007 to 2026
$44.7M
MICHIGAN MEDICAL SCIENTIST TRAINING PROGRAMT32GM007863 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI COLLINS, KATHLEEN L. · 1985 to 2024
$38.3M
Transitioning Early Career Neurosurgeons to Scientific IndependenceK12NS080223 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ESKANDAR, EMAD N · 2012 to 2022
$9.2M
Cortical information loss in diffuse low-grade glioma infiltrated cortexR01NS137950 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Shawn L. Hervey-Jumper · 2024 to 2026
$1.2M
NCI NIH HHS P01 CA118816NIGMS NIH HHS T32 GM007863NINDS NIH HHS K12 NS080223NINDS NIH HHS R01 NS137950
6 · The paper itself

Abstract

A hallmark of glioblastoma (GBM) is disease recurrence that occurs in all patients despite resection, radiation, and chemotherapy. A critical challenge in GBM treatment is the management of recurrent disease for which no standard of care exists. Predicting the location of GBM recurrence may improve the efficiency of advanced-stage therapies. We present an artificial intelligence (AI)-based model to predict the recurrence risk of unprocessed surgical tissues at initial resection. AI-informed label-free optical microscopy was used to generate a normalized tumor infiltration value (AI-infiltration) for optical images of samples taken from resection cavity margins. These values, in combination with clinical, radiographic, and molecular variables, were used to build a predictive model of focal recurrence. In a cohort of 80 patients, comprising 367 samples and 133,454 unique images, GBM infiltration was significantly higher in margin samples from recurrent sites (

Indexed as

Brain NeoplasmsGlioblastomaMicroscopyNeoplasm Recurrence, LocalArtificial IntelligenceFemaleHumansMachine LearningRandom ForestROC Curve

Identifiers

PMID42789696
PMCPMC13614369

What OpenQuestion holds

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Registered trials

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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.