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Conformal predictors in early diagnostics of ovarian and breast cancers

Devetyarov, D; Nouretdinov, I; Burford, B; Camuzeaux, S; Gentry-Maharaj, A; Tiss, A; Smith, C; ... Gammerman, A; + view all (2012) Conformal predictors in early diagnostics of ovarian and breast cancers. Progress in Artificial Intelligence , 1 (3) pp. 245-257. 10.1007/s13748-012-0021-y. Green open access

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Abstract

The paper describes an application of a recently developed machine learning technique called Mondrian predictors to risk assessment of ovarian and breast cancers. The analysis is based on mass spectrometry profiling of human serum samples that were collected in the United Kingdom Collaborative Trial of Ovarian Cancer Screening. The paper describes the technique and presents the results of classification (diagnosis) and the corresponding measures of confidence of the diagnostics. The main advantage of this approach is a proven validity of prediction. The paper also describes an approach to improve early diagnosis of ovarian and breast cancers since the data in the United Kingdom Collaborative Trial of Ovarian Cancer Screening were collected over a period of seven years and do allow to make observations of changes in human serum over that period of time. Significance of improvement is confirmed statistically (for up to 11 months for Ovarian Cancer and 9 months for Breast Cancer). In addition, the methodology allowed us to pinpoint the same mass spectrometry peaks as previously detected as carrying statistically significant information for discrimination between healthy and diseased patients. The results are discussed.

Type: Article
Title: Conformal predictors in early diagnostics of ovarian and breast cancers
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s13748-012-0021-y
Publisher version: http://dx.doi.org/10.1007/s13748-012-0021-y
Language: English
Additional information: The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-012-0021-y.
Keywords: Proteomics, Mass spectrometry, Artificial intelligence, Prediction with confidence, Early diagnosis, Conformal predictors, Ovarian cancer, Breast cancer, Biological markers
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 > 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/1355597
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