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    Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer.

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    NN_NBM-20-0004_Proof_hi.pdf
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    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Fan, Ming cc
    Liu, Zuhui
    Xu, Maosheng
    Wang, Shiwei
    Zeng, Tieyong
    Gao, Xin cc
    Li, Lihua cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-06-10
    Online Publication Date
    2020-06-10
    Print Publication Date
    2020-08
    Embargo End Date
    2021-06-11
    Submitted Date
    2020-01-06
    Permanent link to this record
    http://hdl.handle.net/10754/663539
    
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    Abstract
    Diffusion-weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super-resolution (SR) ADC images and to assess the clinical utility of these SR images by performing a radiomic analysis for predicting the histologic grade and Ki-67 expression status of breast cancer. To this end, 322 samples of dynamic enhanced magnetic resonance imaging (DCE-MRI) and the corresponding DWI data were collected. A SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network along with the bicubic interpolation were utilized to generate SR-ADC images from which radiomic features were extracted. The dataset was randomly separated into a development dataset (n = 222) to establish a deep SR model using DCE-MRI and a validation dataset (n = 100) to improve the resolution of ADC images. This random separation of datasets was performed 10 times, and the results were averaged. The EDSR method was significantly better than the SRGAN and bicubic methods in terms of objective quality criteria. Univariate and multivariate predictive models of radiomic features were established to determine the area under the receiver operating characteristic curve (AUC). Individual features from the tumour SR-ADC images showed a higher performance with the EDSR and SRGAN methods than with the bicubic method and the original images. Multivariate analysis of the collective radiomics showed that the EDSR- and SRGAN-based SR-ADC images performed better than the bicubic method and original images in predicting either Ki-67 expression levels (AUCs of 0.818 and 0.801, respectively) or the tumour grade (AUCs of 0.826 and 0.828, respectively). This work demonstrates that in addition to improving the resolution of ADC images, deep SR networks can also improve tumour image-based diagnosis in breast cancer.
    Citation
    Fan, M., Liu, Z., Xu, M., Wang, S., Zeng, T., Gao, X., & Li, L. (2020). Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer. NMR in Biomedicine. doi:10.1002/nbm.4345
    Sponsors
    This work was supported by the National Natural Science Foundation of China (61731008, 61871428), the Natural Science Foundation of Zhe-jiang Province of China (LJ19H180001), and by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research(OSR) under award no. URF/1/3450–01
    Publisher
    Wiley
    Journal
    NMR in biomedicine
    DOI
    10.1002/nbm.4345
    PubMed ID
    32521567
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.4345
    ae974a485f413a2113503eed53cd6c53
    10.1002/nbm.4345
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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