A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer. (September 2018) - Vázquez, Manuel A.; Mariño, Inés P.; Blyuss, Oleg; Ryan, Andy; Gentry-Maharaj, Aleksandra; Kalsi, Jatinderpal; Manchanda, Ranjit; Jacobs, Ian; Menon, Usha; Zaikin, Alexey
Highlights: We tackle the problem of early detection of ovariance cancer using longitudinal measurements of multiple biomarkers. We compare two different paradigms: Bayesian methods and deep learning. We provide evidence that using multiple biomarkers yields a performance boost as compared to the standard screening test using CA125 alone. 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.