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

Vázquez, MA; Mariño, IP; Blyuss, O; Ryan, A; Gentry-Maharaj, A; Kalsi, J; Manchanda, R; ... Zaikin, A; + view all (2018) A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. Biomedical Signal Processing and Control , 46 pp. 86-93. 10.1016/j.bspc.2018.07.001. Green open access

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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.

Type: Article
Title: A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.bspc.2018.07.001
Publisher version: https://doi.org/10.1016/j.bspc.2018.07.001
Language: English
Additional information: Published under a Creative Commons license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Ovarian cancer, Biomarkers, Deep learning, Recurrent neural networks, Markov chain, Monte Carlo, Gibbs sampling, Change-point detection, Bayesian estimation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Epidemiology and Public Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer
URI: https://discovery.ucl.ac.uk/id/eprint/10053372
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