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    A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. 
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    A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer.

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    Accepted version (367.7Kb)
    Volume
    46
    Pagination
    86 - 93
    DOI
    10.1016/j.bspc.2018.07.001
    Journal
    Biomed Signal Process Control
    ISSN
    1746-8094
    Metadata
    Show full item record
    Abstract
    We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.
    Authors
    Vázquez, MA; Mariño, IP; Blyuss, O; Ryan, A; Gentry-Maharaj, A; Kalsi, J; Manchanda, R; Jacobs, I; Menon, U; Zaikin, A
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/42483
    Collections
    • Centre for Experimental Cancer Medicine [128]
    Language
    eng
    Licence information
    © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
    Copyright statements
    © 2018 The Authors.
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