Evidence map›Paper›PMID 37505107›Full record

ArticleRadiology. Imaging cancer2023

Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment.

Natsuko Onishi, Teffany Joy Bareng, Jessica Gibbs, Wen Li, Elissa R Price, Bonnie N Joe, John Kornak, Laura J Esserman, David C Newitt, Nola M Hylton and 2 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

9 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Natsuko Onishi *From the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0003-4495-9187
Teffany Joy Bareng *From the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.
Jessica GibbsFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0003-1785-0899
Wen LiFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0001-6584-363X
Elissa R PriceFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0001-9572-7795
Bonnie N JoeFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0001-9333-1463
John KornakFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0002-0089-0619
Laura J EssermanFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0001-9202-4568
David C NewittFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0002-1735-6682
Nola M HyltonFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.ORCID 0000-0002-6747-1662
I-SPY 2 Imaging Working GroupFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.
I-SPY 2 Investigator NetworkFrom the Department of Radiology and Biomedical Imaging (N.O., T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of California San Francisco, 550 16th Street, San Francisco, CA 94158.
University of California System · USUniversity of California, San Francisco · US

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
UT Southwestern NCI National Clinical Trials Network (NCTN) LAPS - Supplemental Funds YR4UG1CA233302 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI David Eric Gerber · 2019 to 2026
$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
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 U01 CA225427NCI NIH HHS UG1 CA233302
6 · The paper itself

Abstract

Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (

Indexed as

Breast NeoplasmsFemaleHumansMagnetic Resonance ImagingMiddle AgedNeoadjuvant TherapyProspective StudiesRetrospective StudiesTreatment OutcomeTumor BurdenBreastCancerDynamic Contrast-enhancedMRITumor Response

Identifiers

PMID37505107
PMCPMC10413289
OpenAlexW4385330543

What OpenQuestion holds

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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.