Publication:
Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

dc.contributor.coauthorPanthi, Bikash
dc.contributor.coauthorMohamed, Rania M.
dc.contributor.coauthorAdrada, Beatriz E.
dc.contributor.coauthorCandelaria, Rosalind P.
dc.contributor.coauthorChen, Huiqin
dc.contributor.coauthorHunt, Kelly K.
dc.contributor.coauthorHuo, Lei
dc.contributor.coauthorHwang, Ken-Pin
dc.contributor.coauthorKorkut, Anil
dc.contributor.coauthorLane, Deanna L.
dc.contributor.coauthorLe-Petross, Huong C.
dc.contributor.coauthorLeung, Jessica W. T.
dc.contributor.coauthorLitton, Jennifer K.
dc.contributor.coauthorPashapoor, Sanaz
dc.contributor.coauthorPerez, Frances
dc.contributor.coauthorSon, Jong Bum
dc.contributor.coauthorSun, Jia
dc.contributor.coauthorThompson, Alastair
dc.contributor.coauthorTripathy, Debu
dc.contributor.coauthorValero, Vicente
dc.contributor.coauthorWei, Peng
dc.contributor.coauthorWhite, Jason
dc.contributor.coauthorXu, Zhan
dc.contributor.coauthorYang, Wei
dc.contributor.coauthorZhou, Zijian
dc.contributor.coauthorYam, Clinton
dc.contributor.coauthorRauch, Gaiane M.
dc.contributor.coauthorMa, Jingfei
dc.contributor.departmentKUH (Koç University Hospital)
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorBöge, Medine
dc.contributor.schoolcollegeinstituteKUH (KOÇ UNIVERSITY HOSPITAL)
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2025-01-19T10:29:29Z
dc.date.issued2023
dc.description.abstractEarly prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.openaccessgold, Green Published
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipWe thank Stephanie Deming, Research Medical Library, MD Anderson Cancer Center, for editing the manuscript. This study was supported by the MD Anderson Moon Shots Program and the Robert D. Moreton Distinguished Chair Funds in Diagnostic Radiology.r The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Supported by the NIH/NCI under award number P30CA016672 and the resources from the Department of Biostatistics Resource Group were used.
dc.description.volume13
dc.identifier.doi10.3389/fonc.2023.1264259
dc.identifier.issn2234-943X
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85176116759
dc.identifier.urihttps://doi.org/10.3389/fonc.2023.1264259
dc.identifier.urihttps://hdl.handle.net/20.500.14288/25876
dc.identifier.wos1095400900001
dc.keywordsTriple-negative breast cancer
dc.keywordsDynamic contrast-enhanced MRI
dc.keywordsNeoadjuvant systemic therapy
dc.keywordsRadiomic analysis
dc.keywordsPathologic complete response
dc.language.isoeng
dc.publisherFrontiers Media Sa
dc.relation.grantnoThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. Supported by the NIH/NCI under award number P30CA016672 and the resources from the Department of Biostatistics Resource Group were used. [P30CA016672]; NIH/NCI
dc.relation.ispartofFrontiers in Oncology
dc.subjectOncology
dc.titleLongitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorBöge, Medine
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit1KUH (KOÇ UNIVERSITY HOSPITAL)
local.publication.orgunit2KUH (Koç University Hospital)
local.publication.orgunit2School of Medicine
relation.isOrgUnitOfPublicationf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isOrgUnitOfPublicationd02929e1-2a70-44f0-ae17-7819f587bedd
relation.isOrgUnitOfPublication.latestForDiscoveryf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isParentOrgUnitOfPublication055775c9-9efe-43ec-814f-f6d771fa6dee
relation.isParentOrgUnitOfPublication17f2dc8e-6e54-4fa8-b5e0-d6415123a93e
relation.isParentOrgUnitOfPublication.latestForDiscovery055775c9-9efe-43ec-814f-f6d771fa6dee

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
IR05360.pdf
Size:
9.23 MB
Format:
Adobe Portable Document Format