Evidence map›Paper›PMID 42663779›Full record

ArticleJournal of neuro-oncology2026

Pathology-validated structural and physiologic habitat imaging for differentiating radiation necrosis from tumor recurrence in brain metastases.

Ji Eun Park, Guowen Shao, Shivani Baisiwala, Nakyoung Kim, Amelia Tan, Francesco Sanvito, Andrea Liang, Zexi Wang, Gianluca Nocera, Catalina Raymond and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuro-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

17 authors.

Ji Eun ParkDepartment of Radiology and Radiological Science, Johns Hopkins University, Baltimore, USA.
Guowen ShaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Shivani BaisiwalaDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Nakyoung KimDynapex LLC, Seoul, South Korea.
Amelia TanStanford University, Stanford, CA, USA.
Francesco SanvitoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Andrea LiangUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Zexi WangUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Gianluca NoceraUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Viên Lam LeUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Ho Sung KimDepartment of Radiology, Asan Medical Center, Seoul, Korea, Republic of (South Korea).
Noriko SalamonDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Whitney B Pope *Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA. wpope@mednet.ucla.edu.
Won KimDepartment of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA.
Jingwen Yao *UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA, USA. JingwenYao@mednet.ucla.edu.

Funding

National Research Foundation of Korea (NRF) grant funded by the Korean government RS-2023-00208227 and RS-2025-25429362
6 · The paper itself

Abstract

purposeDifferentiating tumor recurrence from radiation necrosis (RN) after stereotactic radiosurgery (SRS) remains a major diagnostic challenge in brain metastasis. We aimed to validate established MRI-based tumor habitat analysis for distinguishing tumor from RN in an independent cohort with histopathological ground truth. MATERIALS AND

methodsThis retrospective study included 104 patients (104 lesions) with pathologically confirmed recurrent metastatic tumors (n = 68) or RN (n = 36) who underwent structural and physiologic MRI. Tumor habitats were generated using an established unsupervised clustering model applied to normalized T1-weighted enhanced, T2-weighted, apparent diffusion coefficient, and cerebral blood volume maps. Structural habitats (enhancing tissue, solid low-enhancing, nonviable) and physiologic habitats (hypervascular, hypovascular cellular, nonviable) were quantified as absolute volumes and volume fractions. Logistic regression and receiver operating characteristics analysis evaluated the ability to differentiate tumor and RN. Composite habitat scores integrating structural and physiologic habitats were also developed.

resultsRecurrent metastatic tumors showed higher contrast-enhancing volume (P = .006), higher solid low-enhancing habitat volume (P = .029) and fraction (P = .04), higher hypervascular habitat volume (P = .02) and fraction (P = .03), and lower nonviable tissue habitat fractions on structural (P = .003) and physiologic MRI (P = .015), compared with RN. The combined structural and physiologic MRI habitat score showed the highest diagnostic performance (AUC, 0.80; 95% CI: 0.71-0.87; sensitivity, 89.7%; specificity, 58.3%).

conclusionMRI-based tumor habitat analysis provides a pathology-validated approach to distinguish tumor recurrence from radiation necrosis in patients with prior radiation therapy.

Indexed as

BrainBrain NeoplasmsMagnetic Resonance ImagingNeoplasm Recurrence, LocalRadiation InjuriesRadiosurgeryAdultAgedAged, 80 and overDiagnosis, DifferentialFemaleFollow-Up StudiesHumansMaleMiddle AgedNecrosisApparent diffusion coefficientCerebral blood volumeMRIPathologic validationRadiation necrosisStereotactic radiosurgeryTumor habitat analysisVoxel-wise clustering

Identifiers

PMID42663779
PMCPMC13525006

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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.