Evidence map›Paper›PMID 42350576›Full record

ArticleNPJ digital medicine2026

Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.

Jacob Ellison, Nate Tran, Tracy L Luks, Paramjot Singh, Angela Jakary, Tiffany Ngan, Julia Cluceru, Joanna J Phillips, Yan Li, Annette M Molinaro and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Jacob EllisonDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Nate TranDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Tracy L LuksDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Paramjot SinghDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Angela JakaryDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Tiffany NganDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Julia CluceruDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Joanna J PhillipsDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Yan LiDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Annette M MolinaroDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Anny ShaiDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Devika NairDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
Javier E Villanueva-MeyerDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
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.
Manish AghiDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Susan M ChangDepartment of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Janine M LupoDepartment of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA. janine.lupo@ucsf.edu.

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
NIH HHS P01CA118816NIH HHS T32CA151022-14
6 · The paper itself

Abstract

Despite prior success in classifying recurrent glioma noninvasively with multi-parametric MRI and AI, clinical applicability has yet to be demonstrated due to a lack of robust model evaluation and spatial preservation of tumor characteristics. This study develops, robustly evaluates, and clinically validates an interpretable model for predicting recurrent tumors from spatially varying, histopathologically-confirmed tissue samples. Machine learning models were developed using 254 pre-surgical multi-parametric MRI patches surrounding coordinates of tissue samples taken during recurrent surgery. A test AUROC of 0.74 ± 0.08 for distinguishing recurrent tumors, and 0.99 ± 0.01 for normal-appearing brain, demonstrated the feasibility of spatially mapping heterogeneity. Important features were consistent with current literature, and uncertainty was correlated with model failures (p ≤ 0.05). Volumetrics derived from prediction maps of recurrent tumors generated using a separate cohort of 56 patients with recurrent high-grade gliomas were significantly associated with survival. These results demonstrate a step towards clinical applicability of spatially mapping glioma recurrence.

Identifiers

PMID42350576
PMCPMC13669375

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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.