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dc.contributor.authorFan, Ming
dc.contributor.authorZhang, You
dc.contributor.authorFu, Zhenyu
dc.contributor.authorXu, Maosheng
dc.contributor.authorWang, Shiwei
dc.contributor.authorXie, Sangma
dc.contributor.authorGao, Xin
dc.contributor.authorWang, Yue
dc.contributor.authorLi, Lihua
dc.date.accessioned2021-11-03T07:40:26Z
dc.date.available2021-11-03T07:40:26Z
dc.date.issued2021-11-13
dc.date.submitted2021-04-24
dc.identifier.citationFan, M., Zhang, Y., Fu, Z., Xu, M., Wang, S., Xie, S., … Li, L. (2021). A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics. Medical Physics. doi:10.1002/mp.15316
dc.identifier.issn0094-2405
dc.identifier.issn2473-4209
dc.identifier.doi10.1002/mp.15316
dc.identifier.urihttp://hdl.handle.net/10754/673086
dc.description.abstractPurpose :Clinical indicators of histological information are important for breast cancer treatment and operational decision making, but these histological data suffer from frequent missing values due to various experimental/clinical reasons. The limited amount of histological information from breast cancer samples impedes the accuracy of data imputation. The purpose of this study was to impute missing histological data, including Ki-67 expression level, luminal A subtype, and histological grade, by integrating tumor radiomics. Methods : To this end, a deep matrix completion (DMC) method was proposed for imputing missing histological data using nonmissing features composed of histological and tumor radiomics (termed radiohistological features). DMC finds a latent nonlinear association between radiohistological features across all samples and samples for all the features. Radiomic features of morphologic, statistical and texture features were extracted from dynamic enhanced magnetic imaging (DCE-MRI) inside the tumor. Experiments on missing histological data imputation were performed with a variable number of features and missing data rates. The performance of the DMC method was compared with those of the nonnegative matrix factorization (NMF) and collaborative filtering (MCF)-based data imputation methods. The area under the curve (AUC) was used to assess the performance of missing histological data imputation. Results : By integrating radiomics from DCE-MRI, the DMC method showed significantly better performance in terms of AUC than that using only histological data. Additionally, DMC using 120 radiomic features showed an optimal prediction performance (AUC = 0.793), which was better than the NMF (AUC = 0.756) and MCF methods (AUC = 0.706; corrected p = 0.001). The DMC method consistently performed better than the NMF and MCF methods with a variable number of radiomic features and missing data rates. Conclusions : DMC improves imputation performance by integrating tumor histological and radiomics data. This study transforms latent imaging-scale patterns for interactions with molecular-scale histological information and is promising in the tumor characterization and management of patients.
dc.description.sponsorshipThis work was supported in part by the National Key R&D Program of China Under Grant 2018YFA0701700, National Natural Science Foundation of China (61731008, 61871428), the Natural Science Foundation of Zhejiang Province of China (LJ19H180001), and by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award nos. REI/1/0018–01–01, REI/1/4216–01–01, REI/1/4216–01–01, and URF/1/4352–01–01.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/mp.15316
dc.rightsArchived with thanks to Medical Physics
dc.titleA deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalMedical Physics
dc.rights.embargodate2022-11-01
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Biomedical Engineering and Instrumentation Hangzhou Dianzi University Hangzhou China
dc.contributor.institutionDepartment of Radiology First Affiliated Hospital of Zhejiang Chinese Medical University Hangzhou Zhejiang China
dc.contributor.institutionDepartment of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington Virginia USA
kaust.personGao, Xin
kaust.grant.numberREI/1/0018–01–01
kaust.grant.numberREI/1/4216–01–01
kaust.grant.numberURF/1/4352–01–01
dc.date.accepted2021-10-14
refterms.dateFOA2021-11-03T07:41:55Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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