Evidence map›Paper›PMID 36139594›Full record

ArticleCancers2022

Diffusion-Weighted MRI for Predicting Pathologic Complete Response in Neoadjuvant Immunotherapy.

Wen Li, Nu N Le, Natsuko Onishi, David C Newitt, Lisa J Wilmes, Jessica E Gibbs, Julia Carmona-Bozo, Jiachao Liang, Savannah C Partridge, Elissa R Price and 10 more

Abstract read
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Trial
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  11. Non-contrast Breast MR Imaging.Radiologic clinics of North America · 2024
    Review
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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

20 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.
Natsuko OnishiDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0003-4495-9187
David C NewittDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
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
Julia Carmona-BozoDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Jiachao LiangDepartment of Radiology and Biomedical Imaging, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0001-9347-6830
Savannah C PartridgeDepartment of Radiology, University of Washington, 1100 Fairview Ave N, Seattle, Washington, DC 98109, USA.
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.ORCID 0000-0001-9333-1463
John KornakDepartment of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, CA 94158, USA.
Mark Jesus M MagbanuaDepartment of Laboratory Medicine, University of California, 2340 Sutter Street, San Francisco, CA 94115, USA.
Rita NandaDepartment of Medicine, University of Chicago, Chicago, IL 60637, USA.ORCID 0000-0001-5248-0876
Barbara LeStageI-SPY 2 Advocacy Group, 499 Illinois Street, San Francisco, CA 94158, USA.
Laura J EssermanDepartment of Surgery, University of California, 550 16th Street, San Francisco, CA 94158, USA.ORCID 0000-0001-9202-4568
I-Spy Imaging Working GroupDepartment of Radiology, University of Washington, 1100 Fairview Ave N, Seattle, Washington, DC 98109, USA.
I-Spy Investigator NetworkDepartment of Radiology, University of Washington, 1100 Fairview Ave N, Seattle, Washington, DC 98109, USA.
Laura J Van't VeerDepartment of Laboratory Medicine, University of California, 2340 Sutter Street, San Francisco, CA 94115, 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
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
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 CA190299NCI NIH HHS R01 CA227763NCI NIH HHS R01 CA255442NCI NIH HHS U01 CA225427NIH HHS P01 CA210961NIH HHS R01 CA132870NIH HHS R01 CA190299NIH HHS R01 CA255442NIH HHS U01 CA225427
6 · The paper itself

Abstract

This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.

Indexed as

breast cancerdiffusion-weighted MRIimmunotherapyneoadjuvant therapypathologic complete response

Identifiers

PMID36139594
PMCPMC9497087

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.