Evidence map›Paper›PMID 35734265›Full record

ArticlePhysics and imaging in radiation oncology2022

Repeatability of radiotherapy dose-painting prescriptions derived from a multiparametric magnetic resonance imaging model of glioblastoma infiltration.

Caterina Brighi, Niels Verburg, Eng-Siew Koh, Amy Walker, Cathy Chen, Sugendran Pillay, Philip C de Witt Hamer, Farhannah Aly, Lois C Holloway, Paul J Keall and 1 more

Open access · goldAbstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 32% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026
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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

11 authors at 5 institutions in 2 countries.

Caterina BrighiACRF Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, Australia.
Niels VerburgBrain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Eng-Siew KohIngham Institute for Applied Medical Research, Sydney, Australia.
Amy WalkerIngham Institute for Applied Medical Research, Sydney, Australia.
Cathy ChenLiverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
Sugendran PillayLiverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
Philip C de Witt HamerBrain Tumor Center Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Farhannah AlyIngham Institute for Applied Medical Research, Sydney, Australia.
Lois C HollowayIngham Institute for Applied Medical Research, Sydney, Australia.
Paul J KeallACRF Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, Australia.
David E J WaddingtonACRF Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, Australia.
UNSW Sydney · AUThe University of Sydney · AUAmsterdam University Medical Centers · NLCamden and Campbelltown Hospitals · AUCancer Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and purpose: Glioblastoma (GBM) patients have a dismal prognosis. Tumours typically recur within months of surgical resection and post-operative chemoradiation. Multiparametric magnetic resonance imaging (mpMRI) biomarkers promise to improve GBM outcomes by identifying likely regions of infiltrative tumour in tumour probability (TP) maps. These regions could be treated with escalated dose via dose-painting radiotherapy to achieve higher rates of tumour control. Crucial to the technical validation of dose-painting using imaging biomarkers is the repeatability of the derived dose prescriptions. Here, we quantify repeatability of dose-painting prescriptions derived from mpMRI. Materials and methods: TP maps were calculated with a clinically validated model that linearly combined apparent diffusion coefficient (ADC) and relative cerebral blood volume (rBV) or ADC and relative cerebral blood flow (rBF) data. Maps were developed for 11 GBM patients who received two mpMRI scans separated by a short interval prior to chemoradiation treatment. A linear dose mapping function was applied to obtain dose-painting prescription (DP) maps for each session. Voxel-wise and group-wise repeatability metrics were calculated for parametric, TP and DP maps within radiotherapy margins. Results: DP maps derived from mpMRI were repeatable between imaging sessions (ICC > 0.85). ADC maps showed higher repeatability than rBV and rBF maps (Wilcoxon test, p = 0.001). TP maps obtained from the combination of ADC and rBF were the most stable (median ICC: 0.89). Conclusions: Dose-painting prescriptions derived from a mpMRI model of tumour infiltration have a good level of repeatability and can be used to generate reliable dose-painting plans for GBM patients.

Indexed as

ADC, apparent diffusion coefficientCSF, cerebrospinal fluidCTV, clinical target volumeCV, coefficient of variationDose-paintingDP, dose prescriptionDSC, dynamic-susceptibility contrastEORTC, European Organisation for Research and Treatment of CancerFLAIR, fluid-attenuated inverse recoveryGBM, glioblastomaGlioblastomaGTV, gross tumour volumeICC, intraclass correlation coefficientmpMRI, multiparametric MRIMultiparametric MRIPTV, planned target volumeRadiotherapyrBF, relative cerebral blood flowrBV, relative cerebral blood volumeRC, repeatability coefficientRepeatabilitySVZ, subventricular zonesT1CE, T1-weighted post-contrastTP, tumour probabilityVOI, volume of interestΔTP, difference in tumour probability between timepoint 2 and timepoint 1σb2, between-subject varianceσw2, within-subject variance

Identifiers

PMID35734265
PMCPMC9207284
OpenAlexW4282835265

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.