Evidence map›Paper›PMID 39590943›Full record

ArticleTomography (Ann Arbor, Mich.)2024

Tumor Morphology for Prediction of Poor Responses Early in Neoadjuvant Chemotherapy for Breast Cancer: A Multicenter Retrospective Study.

Wen Li, Nu N Le, Rohan Nadkarni, Natsuko Onishi, Lisa J Wilmes, Jessica E Gibbs, Elissa R Price, Bonnie N Joe, Rita A Mukhtar, Efstathios D Gennatas and 6 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2024. 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

16 authors.

Wen LiDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0001-6584-363X
Nu N LeDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Rohan NadkarniDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Natsuko OnishiDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0003-4495-9187
Lisa J WilmesDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Jessica E GibbsDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0003-1785-0899
Elissa R PriceDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Bonnie N JoeDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Rita A MukhtarDepartment of Surgery, University of California, San Francisco, 550 16th Street, San Francisco, CA 94158, USA.
Efstathios D GennatasDepartment of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94158, USA.
John KornakDepartment of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, San Francisco, CA 94158, USA.
Mark Jesus M MagbanuaDepartment of Laboratory Medicine, University of California, San Francisco, 2340 Sutter Street, San Francisco, CA 94115, USA.ORCID 0000-0003-2113-3593
Laura J Van't VeerDepartment of Laboratory Medicine, University of California, San Francisco, 2340 Sutter Street, San Francisco, CA 94115, USA.
Barbara LeStageI-SPY 2 Advocacy Group, San Francisco, CA 94158, USA.
Laura J EssermanDepartment of Surgery, University of California, San Francisco, 550 16th Street, San Francisco, CA 94158, USA.
Nola M HyltonDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, 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 CA225427
6 · The paper itself

Abstract

backgroundThis multicenter and retrospective study investigated the additive value of tumor morphologic features derived from the functional tumor volume (FTV) tumor mask at pre-treatment (T0) and the early treatment time point (T1) in the prediction of pathologic outcomes for breast cancer patients undergoing neoadjuvant chemotherapy.

methodsA total of 910 patients enrolled in the multicenter I-SPY 2 trial were included. FTV and tumor morphologic features were calculated from the dynamic contrast-enhanced (DCE) MRI. A poor response was defined as a residual cancer burden (RCB) class III (RCB-III) at surgical excision. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance. The analysis was performed in the full cohort and in individual sub-cohorts stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status.

resultsIn the full cohort, the AUCs for the use of the FTV ratio and clinicopathologic data were 0.64 ± 0.03 (mean ± SD [standard deviation]). With morphologic features, the AUC increased significantly to 0.76 ± 0.04 (

conclusionTumor morphologic features can improve the prediction of RCB-III compared to using FTV only at the early treatment time point.

Indexed as

Breast NeoplasmsMagnetic Resonance ImagingNeoadjuvant TherapyAdultAgedChemotherapy, AdjuvantContrast MediaFemaleHumansMiddle AgedRetrospective StudiesTreatment OutcomeTumor BurdenContrast Mediabreast cancermagnetic resonance imagingmulticenter clinical trialneoadjuvant therapyresidual cancer burdentumor morphology

Identifiers

PMID39590943
PMCPMC11598075

What OpenQuestion holds

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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.