A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE-MRI radiomics

Ming Fan, You Zhang, Zhenyu Fu, Maosheng Xu, Shiwei Wang, Sangma Xie, Xin Gao, Yue Wang, Lihua Li

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Purpose :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.
Original languageEnglish (US)
JournalMedical Physics
StatePublished - Nov 1 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-11-04
Acknowledged KAUST grant number(s): REI/1/0018–01–01, REI/1/4216–01–01, URF/1/4352–01–01
Acknowledgements: This 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.

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Biophysics


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