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Advances in Bioinformatics
Volume 2011, Article ID 172615, 8 pages
Research Article

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier

1Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada K7L 3N6
2Division of Signaling Biology, IBM Life Sciences Discovery Centre, Toronto Medical Discovery Tower, 9-305, 101 College Street, Toronto, Ontario, Canada M5G 1L7
3Departments of Oncology, Medicine, Pharmacology and Toxicology, Queen’s University, Kingston, ON, Canada K7L 5P9

Received 21 September 2011; Revised 16 December 2011; Accepted 31 December 2011

Academic Editor: Shandar Ahmad

Copyright © 2011 Elize A. Shirdel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher’s exact test probability [2 tailed], Matthews’ correlation coefficient +0.83). Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment.