Evidence map›Paper›PMID 41275453›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Novel 4D radiomics applied to dynamic FES PET images to improve prediction of breast cancer response to ER-targeted therapy.

Andrew William Chen, Carla R Zeballos Torrez, Lanell M Peterson, Mark Muzi, Jennifer M Specht, Eric A Cohen, Hannah M Linden, Despina Kontos, David A Mankoff

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PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

9 authors.

Andrew William Chen *Department of Radiology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA.
Carla R Zeballos Torrez *Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Donner Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
Lanell M PetersonDepartment of Radiology, University of Washington, Seattle, WA, USA.
Mark MuziDepartment of Radiology, University of Washington, Seattle, WA, USA.
Jennifer M SpechtDepartment of Hematology & Oncology, University of Washington, Seattle, WA, USA.
Eric A CohenDepartment of Radiology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA.
Hannah M LindenDepartment of Hematology & Oncology, University of Washington, Seattle, WA, USA.
Despina KontosDepartment of Radiology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA.
David A MankoffDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Donner Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. David.Mankoff@pennmedicine.upenn.edu.ORCID 0000-0002-6971-5413

Funding

Core Laboratory for Advanced PET and MRI image analysisR50CA211270 · NCI · UNIVERSITY OF WASHINGTON · PI Mark Muzi · 2017 to 2026
$2.6M
NCI NIH HHS P01042045NCI NIH HHS R50 CA211270Susan G. Komen SAC232145
6 · The paper itself

Abstract

purpose[

methodsWe utilized the Rad-Fit method, previously tested in an FDG PET data set, to identify and characterize intratumor subregions of heterogeneous time-activity through an unsupervised clustering approach. A scaled silhouette score was implemented to determine the optimal number of intratumor subregions on a per-tumor basis. Summary statistics of sum of squared error (SSE) and distance between sub regions as well as the total number of intratumor subregions were used to build prognostic models of overall survival (OS) and progression free survival (PFS). We employed Kaplan-Meyer analysis to determine model performance.

resultsThe radiomic phenotype differentiated between a high and low risk group for progression free survival (C = 0.67, p = 0.025) in the single tumor scenario. Radiomic features of subregion distance classified a high and low risk group for OS in a single tumor (C = 0.67, p = 0.008) and average tumor (C = 0.65, p = 0.017) scenario.

conclusionsIn this exploratory study, 4D radiomic features extracted from dynamic FES PET images can improve the prediction of outcomes in metastatic ER positive breast cancer. Metrics of tumor subregion distance and radiomic phenotype appear to perform as the best radiomic predictors for risk stratification of OS and PFS respectively by potentially reflecting characteristics of the overall tumor heterogeneity in FES PET images. CLINICAL TRIAL NUMBER: not applicable.

Indexed as

Breast NeoplasmsEstradiolImage Processing, Computer-AssistedMolecular Targeted TherapyPositron-Emission TomographyReceptors, EstrogenAdultAgedFemaleHumansMiddle AgedPrognosisRadiomicsTreatment Outcome16-fluoroestradiolEstradiolReceptors, EstrogenDynamic PETEndocrine therapyFluoroestradiolMetastatic breast cancerRadiomics

Identifiers

PMID41275453
PMCPMC13013131

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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.