Evidence map›Paper›PMID 42750922›Full record

ReviewNeuro-oncology practice2026

Automated imaging response evaluation system: Prototype software tool for standardized radiographic response assessment in glioblastoma clinical trials.

Ashley Teraishi, Francesco Sanvito, Catalina Raymond, Nicholas S Cho, Jason Norris, Sani Gandhi, Alexander R Cohen, Yashaar Hafizka, Audrey Luo, Hao-Wen Sim and 16 more

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Neuro-oncology practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04195139 (A Randomised Phase II Study of NivolUmab and TeMozolomide vs Temozolomide Alone in Newly Diagnosed Elderly Patients With Glioblastoma), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT04195139 phase2active not recruitingnot on this map

A Randomised Phase II Study of NivolUmab and TeMozolomide vs Temozolomide Alone in Newly Diagnosed Elderly Patients With Glioblastoma (NUTMEG)

TypeinterventionalSponsorUniversity of SydneyRan2018 to 2025Enrolled103ConditionsGlioblastoma MultiformeArmsNivolumab, Temozolomide
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. 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

26 authors.

Ashley TeraishiUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0009-0004-3442-8109
Francesco SanvitoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0003-3379-9958
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0003-2757-439X
Nicholas S ChoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
Jason NorrisDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Sani GandhiDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Alexander R CohenDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Yashaar HafizkaUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
Audrey LuoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
Hao-Wen SimNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
Lucas P WachsmuthThe Brain Tumor Immunotherapy Program, Duke University School of Medicine, Duke University Medical Center, Durham, NC, USA.
Sonia YipNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
Eng-Siew KohSouth Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.ORCID https://orcid.org/0000-0002-1664-2513
Merryn HallNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
David M AshleyThe Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Duke University Medical Center, Durham, NC, USA.
Mark A RosenthalDepartment of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia.
Elizabeth J HoveySouth Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Hui K GanDepartment of Medical Oncology, Austin Hospital, Melbourne, Australia.
Margaret O JohnsonThe Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Duke University Medical Center, Durham, NC, USA.
Elizabeth H BarnesNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
John R SimesNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.
Zarnie LwinDepartment of Medical Oncology, Royal Brisbane and Women's Hospital, Brisbane, Australia.
Mustafa KhasrawNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia.ORCID https://orcid.org/0000-0003-3249-9849
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-3520-9467
Timothy F CloughesyDepartment of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-2764-6640

Funding

UCLA-Caltech Medical Scientist Training ProgramT32GM008042 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AJIJOLA, OLUJIMI A, DAWSON, DAVID WAYNE · 1985 to 2023
$29.9M
UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Robert M Prins · 2017 to 2026
$25.2M
UCLA-Caltech Medical Scientist Training ProgramT32GM152342 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Olujimi A Ajijola, David Wayne Dawson · 2024 to 2026
$5.3M
Role of decorin and diffusion MRI in anti-VEGF efficacy for recurrent glioblastomaR01CA270027 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TIMOTHY CLOUGHESY, Benjamin M. Ellingson · 2022 to 2026
$3.0M
Quantitative molecular MR-PET imaging of glycolysis in glioblastomaR01CA279984 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Benjamin M. Ellingson, David A. Nathanson · 2023 to 2026
$2.4M
Molecular MR-PET to characterize malignant transformation and identify therapeutic vulnerabilities in human IDH-mutant gliomasF30CA284809 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHO, NICHOLAS S. · 2023 to 2025
$136k
NCI NIH HHS F30 CA284809NCI NIH HHS P50 CA211015NCI NIH HHS R01 CA270027NCI NIH HHS R01 CA279984NIGMS NIH HHS T32 GM008042NIGMS NIH HHS T32 GM152342
6 · The paper itself

Abstract

The clinical management of patients with brain tumors, as well as the results of clinical trials testing new medications, require the evaluation of tumor changes on MR imaging. After tumor measurements are obtained, the AIRES software application can automatically apply the standardized RANO rules, to ultimately define when the tumor can be considered in a progression phase, as opposed to a stable phase, or in treatment response. This automated tool enables fast, reliable, and reproducible assessments, and therefore has the potential to reduce errors, streamline workflows, and lower costs by minimizing the resources required for training personnel and conducting reads. The standardized Response Assessment in Neuro-Oncology (RANO), modified RANO (mRANO), and RANO 2.0 criteria are utilized for imaging endpoints in glioblastoma clinical trials. After measuring tumor size at each timepoint, the crucial step of categorizing progression, stable disease, and response based on numerical thresholds is currently done manually. "Manual" RANO evaluations are time-consuming, error prone, and require dedicated training. Here, we present the features, installation, and usage of Automated Imaging Response Evaluation System (AIRES), a software application prototype that applies mRANO criteria using externally measured tumor sizes, clinical status, and corticosteroid dose. AIRES labels each timepoint with RANO categories and calculates progression-free survival and objective response rate. As a real-world application on clinical trial datasets, mRANO reads were performed on measurements from 367 brain MRI scans across 41 patients from NUTMEG (NCT04195139), a phase II multicenter clinical trial. Four readers with varying levels of experience achieved a percentage of fully correct RANO reads ranging from 49% to 76%, while AIRES reached 100%, compared to a fifth expert reader. AIRES-based reads were significantly faster than "manual" reads by the expert (37.1 ± 8.6 vs 107.7 ± 58.6 s,

Identifiers

PMID42750922
PMCPMC13578499

What OpenQuestion holds

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Registered trials

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.