Evidence map›Paper›PMID 41228352›Full record

ArticleCancers2025

Predicting Remaining Survival of Glioblastoma Patients with Radiomics Analysis Based on

Jing Qian, Deanna Hasenauer, William G Breen, Paul D Brown, Christopher H Hunt, Mark S Jacobson, Derek R Johnson, Timothy J Kaufmann, Bradley J Kemp, Sani H Kizilbash and 11 more

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

21 authors.

Jing QianDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-5276-4158
Deanna HasenauerDepartment of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-0768-7277
William G BreenDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Paul D BrownDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Christopher H HuntDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-0301-0493
Mark S JacobsonDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Derek R JohnsonDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-4217-5517
Timothy J KaufmannDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-7483-1569
Bradley J KempDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-2360-2368
Sani H KizilbashDepartment of Medical Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Val J LoweDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-5612-1667
Michael W RuffDepartment of Neurology, Mayo Clinic, Rochester, MN 55905, USA.
Jann N SarkariaDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-7489-4885
Joon H UhmDepartment of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA.
Mark J ZakharyUniversity of Florida Health Proton Therapy Institute, Jacksonville, FL 32206, USA.ORCID 0000-0003-4333-648X
Maasa H SeabergDepartment of Radiation Oncology, University of California San Francisco Medical Center, San Francisco, CA 94143, USA.ORCID 0000-0002-7655-1111
Hok Seum Wan Chan TseungDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Elizabeth S YanDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
Yan ZhangDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-3418-9385
Nadia N LaackDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-4385-6349
Debra H BrinkmannDepartment of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-0812-3094

Funding

Center for Clinical and Translational Science of Mayo Clinic 2022 Advance the Practice Research AwardNIH HHS 1R01CA178200-18
6 · The paper itself

Abstract

backgroundPost-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer

methods

resultsThe ML models exhibited 81-83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial

conclusionsOur study demonstrates that ML models utilizing FU

Indexed as

18F-DOPAamino-acid PET tracerglioblastomamachine learningmanifold learningpost-treatment follow-upradiomicsremaining survival

Identifiers

PMID41228352
PMCPMC12607425

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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.