Evidence map›Paper›PMID 40523904›Full record

ArticleNPJ precision oncology2025

Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas.

Taishi Nakase, George A Henderson, Thomas Barba, Rohan Bareja, Geno Guerra, Qingyu Zhao, Stephen S Francis, Olivier Gevaert, Linda Kachuri

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

9 authors.

Taishi NakaseDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3078-098X
George A HendersonDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
Thomas BarbaStanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, USA.
Rohan BarejaStanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, USA.
Geno GuerraDepartment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
Qingyu ZhaoDepartment of Radiology, Weill Cornell Medicine, New York, NY, USA.
Stephen S FrancisDepartment of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-6488-6272
Olivier GevaertStanford Center for Biomedical Informatics Research (BMIR), Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9965-5466
Linda KachuriDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. lkachuri@stanford.edu.ORCID http://orcid.org/0000-0002-3226-4727

Funding

Comprehensive Investigation of Multiple Myeloma Genetic Susceptibility in African AmericansR00CA246076 · NCI · STANFORD UNIVERSITY · PI KACHURI, MIRLINDA · 2022 to 2024
$747k
NCI NIH HHS R00 CA246076
6 · The paper itself

Abstract

The molecular profiling of gliomas for isocitrate dehydrogenase (IDH) mutations currently relies on resected tumor samples, highlighting the need for non-invasive, preoperative biomarkers. We investigated the integration of glioma polygenic risk scores (PRS) and radiographic features for prediction of IDH mutation status. We used 256 radiomic features, a glioma PRS and demographic information in 158 glioma cases within elastic net and neural network models. The integration of glioma PRS with radiomics increased the area under the receiver operating characteristic curve (AUC) for distinguishing IDH-wildtype vs. IDH-mutant glioma from 0.83 to 0.88 (P

Identifiers

PMID40523904
PMCPMC12170908

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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.