Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer

Authors:
Alex Gammerman;Volodya Vovk;Brian Burford;Ilia Nouretdinov;Zhiyuan Luo;Alexey Chervonenkis;Mike Waterfield;Rainer Cramer;Paul Tempst;Josep Villanueva;Musarat Kabir;Stephane Camuzeaux;John Timms;Usha Menon;Ian Jacobs
Affiliations:
-;-;-;-;-;-;-;-;-;-;-;-;-;-;-
Venue:
The Computer Journal
Year:
2009

Citing 0
Cited 5

Online Prediction of Ovarian Cancer

AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Neural networks regression inductive conformal predictor and its application to total electron content prediction

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Regression conformal prediction with nearest neighbours

Journal of Artificial Intelligence Research
2011 Special Issue: Reliable prediction intervals with regression neural networks

Neural Networks
Reliable probabilistic classification with neural networks

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ovarian cancer is characterized by vague, non-specific symptoms, advanced stage at diagnosis and poor overall survival. A nested case control study was undertaken on stored serial serum samples from women who developed ovarian cancer and healthy controls (matched for serum processing and storage conditions as well as attributes such as age) in a pilot randomized controlled trial of ovarian cancer screening. The unique feature of this study is that the women were screened for up to 7 years. The serum samples underwent prefractionation using a reversed-phase batch extraction protocol prior to MALDI-TOF MS data acquisition. Our exploratory analysis shows that combining a single MS peak with CA125 allows statistically significant discrimination at the 5% level between cases and controls up to 12 months in advance of the original diagnosis of ovarian cancer. Such combinations work much better than a single peak or CA125 alone. This paper demonstrates that mass spectra from the low molecular weight serum proteome carry information useful for early detection of ovarian cancer. The next step is to identify the specific biomarkers that make early detection possible.