Evidence map›Paper›PMID 40158731›Full record

SynthesisInternational journal of radiation oncology, biology, physics2025

Timing of Planning Magnetic Resonance Imaging and Patient Selection for Adaptive Radiation Therapy in Newly Diagnosed High-Grade Glioma.

Nicholas C DeWalt, Theodore K Yanagihara, Matthew Gallitto, Connor J Kinslow, Codruta Chiuzan, Yuanguang Xu, Gagandeep Singh, Jeffrey N Bruce, Tony J C Wang, Jack Grinband

Abstract readMeta-Analysis
In one paragraph

Synthesis in International journal of radiation oncology, biology, physics, 2025. 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

10 authors.

Nicholas C DeWaltDepartment of Neurological Surgery, Columbia University Irving Medical Center, New York, New York.
Theodore K YanagiharaDepartment of Radiation Oncology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina.
Matthew GallittoDepartment of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.
Connor J KinslowDepartment of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.
Codruta ChiuzanInstitute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New York, New York.
Yuanguang XuDepartment of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.
Gagandeep SinghDepartment of Radiology, Neuroradiology Division, Columbia University Irving Medical Center, New York, New York.
Jeffrey N BruceDepartment of Neurological Surgery, Columbia University Irving Medical Center, New York, New York.
Tony J C WangDepartment of Radiation Oncology, Columbia University Irving Medical Center, New York, New York.
Jack GrinbandDepartment of Radiology, Columbia University Irving Medical Center, New York, New York; Department of Psychiatry, Columbia University Irving Medical Center, New York, New York. Electronic address: Jg2269@cumc.columbia.edu.

Funding

Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
CAPRI: Columbia Cancer Research Training Program for Resident-InvestigatorsR38CA277850 · NCI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Katherine D Crew, Lisa A. Kachnic · 2024 to 2026
$1.6M
NCATS NIH HHS UL1 TR000040NCI NIH HHS R38 CA277850
6 · The paper itself

Abstract

purposeRadiation therapy (RT) planning for high-grade glioma (HGG) typically relies on a single postoperative magnetic resonance image (MRI), assuming stable tumor and brain architecture throughout treatment. Though the gross tumor volume and surrounding tissues can shift after surgery and throughout RT, its rate of change, predictors of the movement magnitude, and impact on different dosing strategies are not well understood. This study uses meta-analysis and prospective MRI data to optimize MRI timing and identify patients who may benefit from adaptive RT (ART). METHODS AND MATERIALS: We performed a meta-analysis of 12 studies (405 patients) to quantify postresection brain morphology changes. Additionally, we prospectively collected MRIs from 17 newly diagnosed HGG patients who underwent resection and RT. Nonlinear image registration tracked voxel-wise movement from postsurgery through RT and follow-up. We analyzed changes in gross tumor volume, clinical target volume (CTV), and non-CTV influx volume (nCIV) across 8 CTV strategies. Exponential models predicted the magnitude and rate of postresection changes and optimal MRI timing.

resultsBoth meta-analysis and prospective data showed that morphological changes followed an exponential decay, with 80% of shifts occurring within ∼30 days of resection. The nCIV was strongly predicted by T2-weighted fluid-attenuated inversion recovery volume and CTV margin strategy (86% accuracy for T1-based and 80% for T2-weighted fluid-attenuated inversion recovery-based CTVs). To minimize nCIV in patients starting RT >3 weeks postsurgery, our model suggests acquiring an additional MRI before RT. Otherwise, RT planning should be adaptive.

conclusionsAcquiring a delayed planning MRI or performing ART based on recent MRIs may optimize RT delivery for patients with larger tumors and specific CTV strategies. We propose a simple, clinically feasible algorithm for selecting patients and determining optimal MRI timing in prospective clinical trials of HGG ART.

Indexed as

Brain NeoplasmsGliomaMagnetic Resonance ImagingPatient SelectionRadiotherapy Planning, Computer-AssistedHumansMaleMiddle AgedMovementProspective StudiesTime FactorsTumor Burden

Identifiers

PMID40158731
PMCPMC12146077

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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.