Evidence map›Paper›PMID 42089524›Full record

ReviewAJR. American journal of roentgenology2026

The Future of Quantitative Breast Imaging: Integrated to Integral Imaging Biomarkers for Precision Cancer Care, From the

Elizabeth S McDonald, Anum S Kazerouni, Pouya Metanet, Lev Barinov, Savannah C Partridge, Nola M Hylton

Abstract readReview
In one paragraph

Review in AJR. American journal of roentgenology, 2026. 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

5 · Who and what money

Authors and funding

6 authors.

Elizabeth S McDonaldDepartment of Radiology, University of Pennsylvania.
Anum S KazerouniDepartment of Radiology, University of Washington.
Pouya MetanetDepartment of Radiology and Biomedical Imaging, University of California, San Francisco.
Lev BarinovDepartment of Radiology, University of Pennsylvania.
Savannah C PartridgeDepartment of Radiology, University of Washington.
Nola M HyltonDepartment of Radiology and Biomedical Imaging, University of California, San Francisco.

Funding

The I SPY 2.2 TRIAL: Evolving to Imaging and Molecular Biomarker Response Directed Adaptive Sequential Treatment to Optimize Breast Cancer OutcomesP01CA210961 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nola M. Hylton-Watson · 2017 to 2026
$22.9M
Fred Hutchinson Breast Cancer Clinical Validation CenterU01CA152637 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Christopher I Li, Savannah Corrina Partridge · 2010 to 2026
$14.6M
Elimination of Instrumental Bias for Quantitative Diffusion Imaging in Clinical Oncology TrialsR01CA190299 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MALYARENKO, DARIYA I. · 2015 to 2025
$6.4M
Real-time In Vivo MRI Biomarkers for Breast Cancer Pre-Operative Treatment TrialsR01CA132870 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HYLTON-WATSON, NOLA M., NEWITT, DAVID C · 2008 to 2019
$4.8M
Dedicated breast PET and MRI for characterization of breast cancer and its response to therapyR01CA227763 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Natsuko Onishi Yamashita · 2019 to 2026
$3.6M
Quantitative Imaging for Assessing Breast Cancer Response to TreatmentU01CA225427 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HYLTON-WATSON, NOLA M. · 2018 to 2022
$3.2M
Tuning PARP-1 retention and release on DNA breaksR01CA259037 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Ben E. Black, John M Pascal · 2022 to 2026
$2.9M
Multicenter Quantitative MRI Assessment of Breast Cancer Therapy ResponseR01CA248192 · NCI · WILLIAM BEAUMONT HOSPITAL RESEARCH INST · PI WEI HUANG, James H Holmes · 2021 to 2026
$2.8M
Developing PARP-1 PET with companion tissue assay as a precision tool to guide PARPi therapyR37CA277541 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Elizabeth McDonald · 2024 to 2026
$1.6M
Quantitative characterization of tumor heterogeneity using habitat imaging for the prediction of patient outcome in triple negative breast cancerK99CA293004 · NCI · UNIVERSITY OF WASHINGTON · PI KAZEROUNI, ANUM SYED · 2024 to 2025
$203k
NCI NIH HHS K99 CA293004NCI NIH HHS P01 CA210961NCI NIH HHS R01 CA132870NCI NIH HHS R01 CA190299NCI NIH HHS R01 CA227763NCI NIH HHS R01 CA248192NCI NIH HHS R01 CA259037NCI NIH HHS R37 CA277541NCI NIH HHS U01 CA152637NCI NIH HHS U01 CA225427
6 · The paper itself

Abstract

This review explores the potential of quantitative imaging biomarkers (QIBs) for guiding personalized breast cancer treatment through prognostic stratification and measurement of treatment response. Accumulating data from prospective clinical trials show that QIBs are more predictive of treatment outcome than conventional sizebased measures (e.g., change in tumor diameter), highlighting these markers' potential clinical impact. Although QIBs have shown utility in the trial setting, clinical adoption has remained limited. In this article, we present biomarkers derived from dynamic contrast-enhanced MRI, DWI, [18F]FDG PET, and [18F]FES PET, and highlight implementation challenges including variable acquisition protocols, lack of standardization, and uncertainty around optimal timing of imaging during treatment. We distinguish between integrated biomarkers-those used for correlative or exploratory purposes within a trial-and integral biomarkers-those that are essential to trial design and directly inform patient stratification or therapeutic decisions. Standardization efforts by national and international organizations are underway to ensure imaging marker reliability, and commercial tools supporting clinical translation have become available. Dedicated end-to-end solutions remain needed to integrate QIBs into clinical workflows and ultimately into standard care pathways, to promote these markers' role in patient stratification and adaptive treatment decisions.

Identifiers

PMID42089524
PMCPMC13682680

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.