ArticleTomography (Ann Arbor, Mich.)2026
Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.
Wen Li, Lisa J Wilmes, Julia Carmona-Bozo, Nu N Le, Maggie Chung, Jessica E Gibbs, Natsuko Onishi, Elissa Price, Bonnie N Joe, John Kornak and 5 more
Abstract readMulticenter Study
In one paragraphArticle in Tomography (Ann Arbor, Mich.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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What it found
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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 registryThe trial behind it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
15 authors.
Wen LiDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0001-6584-363X Lisa J WilmesDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Julia Carmona-BozoDepartment 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.
Maggie ChungDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
Jessica E GibbsDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0003-1785-0899 Natsuko OnishiDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.ORCID 0000-0003-4495-9187 Elissa 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.ORCID 0000-0002-0089-0619 Thomas L ChenevertDepartment of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
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
$22.9MElimination 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
$6.4MReal-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
$4.8MStrategy 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
$3.3MQuantitative Imaging for Assessing Breast Cancer Response to TreatmentU01CA225427 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HYLTON-WATSON, NOLA M. · 2018 to 2022
$3.2MNCI NIH HHS P01 CA210961NCI NIH HHS R01 CA132870NCI NIH HHS R01 CA190299NCI NIH HHS R01 CA255442NCI NIH HHS U01 CA225427
6 · The paper itselfAbstract
BACKGROUND/
objectivesApparent diffusion coefficient (ADC) calculated from diffusion-weighted MRI (DWI) can predict tumor response to neoadjuvant chemotherapy for breast cancer. However, obtaining consistently adequate image quality in breast DWI can be challenging, and the effect of image quality on ADC's predictive performance is unclear. The objective of this study was to evaluate inter-reader variability in image quality assessment and the effect of DWI image quality on the predictive performance of ADC.
methodsThis multi-institutional study included 428 patients. Two readers assessed three DWI image quality factors-fat suppression, artifacts, and signal-to-noise ratio (SNR). Inter-reader agreement was estimated using Fleiss' Kappa. The percent change in tumor ADC from pretreatment (T0) to early treatment (T1) was used to predict pathologic complete response (pCR), assessed at surgery.
resultsOut of 428 patients, 134 were excluded (missing pCR [
conclusionsThe inter-reader agreement was moderate to fair across all three quality categories. When a manually delineated tumor ROI was possible, no statistically significant difference in ADC predictive performance was observed between the quality-adequate and quality-inadequate cohorts; still, both were predictive of pCR. Furthermore, no statistically significant differences were observed in inter-reader agreement or ADC predictive performance between 1.5T and 3T scanners. These findings are clinically relevant to the use of ADC as an imaging biomarker in real-world conditions.
Indexed as
Breast NeoplasmsDiffusion Magnetic Resonance ImagingNeoadjuvant TherapyAdultAgedArtifactsChemotherapy, AdjuvantFemaleHumansMiddle AgedObserver VariationPathologic Complete ResponsePredictive Value of TestsRetrospective StudiesSignal-To-Noise RatioTreatment Outcomeapparent diffusion coefficientartifactbreast cancerdiffusion-weighted MRIfat suppressionimage qualitypathologic complete responsesignal-to-noise ratio
Identifiers
PMID42347142
PMCPMC13306497
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