Evidence map›Paper›PMID 40137566›Full record

ArticleTomography (Ann Arbor, Mich.)2025

MRI-Based Model for Personalizing Neoadjuvant Treatment in Breast Cancer.

Wen Li, Natsuko Onishi, Jessica E Gibbs, Lisa J Wilmes, Nu N Le, Pouya Metanat, Elissa R Price, Bonnie N Joe, John Kornak, Christina Yau and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. Review
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.

Wen LiDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0001-6584-363X
Natsuko OnishiDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0003-4495-9187
Jessica E GibbsDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0003-1785-0899
Lisa J WilmesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Nu N LeDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Pouya MetanatDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Elissa R PriceDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Bonnie N JoeDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0001-9333-1463
John KornakDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
Christina YauDepartment of Surgery, University of California, San Francisco, CA 94158, USA.
Denise M WolfDepartment of Laboratory Medicine, University of California, San Francisco, CA 94158, USA.
Mark Jesus M MagbanuaDepartment of Laboratory Medicine, University of California, San Francisco, CA 94158, USA.ORCID 0000-0003-2113-3593
Barbara LeStageI-SPY 2 Advocacy Group, San Francisco, CA 94158, USA.
Laura J van 't VeerDepartment of Laboratory Medicine, University of California, San Francisco, CA 94158, USA.
Angela M DeMicheleDepartment of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Laura J EssermanDepartment of Surgery, University of California, San Francisco, CA 94158, USA.ORCID 0000-0001-9202-4568
Nola M HyltonDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.

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
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
Strategy for combining circulating tumor DNA (ctDNA) and magnetic resonance imaging (MRI) measures of tumor burden for prediction of response and outcome in neoadjuvant-treated early breast cancerR01CA255442 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LI, WEN, MAGBANUA, MARK JESUS · 2021 to 2025
$3.3M
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
NCI NIH HHS P01 CA210961NCI NIH HHS R01 CA132870NCI NIH HHS R01 CA255442NCI NIH HHS U01 CA225427NIH HHS P01 CA210961NIH HHS R01 CA132870NIH HHS R01 CA255442NIH HHS U01 CA225427
6 · The paper itself

Abstract

backgroundFunctional tumor volume (FTV), measured from dynamic contrast-enhanced MRI, is an imaging biomarker that can predict treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The FTV-based predictive model, combined with core biopsy, informed treatment decisions of recommending patients with excellent responses to proceed to surgery early in a large NAC clinical trial.

methodsIn this retrospective study, we constructed models using FTV measurements. We analyzed performance tradeoffs when a probability threshold was used to identify excellent responders through the prediction of pathology complete response (pCR). Individual models were developed within cohorts defined by the hormone receptor and human epidermal growth factor receptor 2 (HR/HER2) subtype.

resultsA total of 814 patients enrolled in the I-SPY 2 trial between 2010 and 2016 were included with a mean age of 49 years (range: 24 to 77). Among these patients, 289 (36%) achieved pCR. The area under the ROC curve (AUC) ranged from 0.68 to 0.74 for individual HR/HER2 subtypes. When probability thresholds were chosen based on minimum positive predictive value (PPV) levels of 50%, 70%, and 90%, the PPV-sensitivity tradeoff varied among subtypes. The highest sensitivities (100%, 87%, 45%) were found in the HR-/HER2+ sub-cohort for probability thresholds of 0, 0.62, and 0.72; followed by the triple-negative sub-cohort (98%, 52%, 4%) at thresholds of 0.13, 0.58, and 0.67; and HR+/HER2+ (78%, 16%, 8%) at thresholds of 0.34, 0.57, and 0.60. The lowest sensitivities (20%, 0%, 0%) occurred in the HR+/HER2- sub-cohort.

conclusionsPredictive models developed using imaging biomarkers, alongside clinically validated probability thresholds, can be incorporated into decision-making for precision oncology.

Indexed as

Breast NeoplasmsMagnetic Resonance ImagingNeoadjuvant TherapyPrecision MedicineAdultAgedBreastErb-b2 Receptor Tyrosine KinasesFemaleHumansMiddle AgedPredictive Value of TestsRetrospective StudiesROC CurveTumor BurdenYoung AdultERBB2 protein, humanErb-b2 Receptor Tyrosine Kinasesbreast cancerdynamic contrast-enhanced MRIfunctional tumor volumeneoadjuvant chemotherapypathologic complete responsetherapy decision

Identifiers

PMID40137566
PMCPMC11946387

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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.