Cancer subtype identification pipeline: a classifusion approach

Agrawal, Utkarsh; Soria, Daniele; Wagner, Christian

Abstract

Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers.

Citation

Agrawal, U., Soria, D., & Wagner, C. (2016). Cancer subtype identification pipeline: a classifusion approach.

• Conference Name: 2016 IEEE Congress on Evolutionary Computation (CEC)
• End Date: Jul 29, 2016
• Acceptance Date: Mar 16, 2016
• Publication Date: Nov 21, 2016
• Deposit Date: Dec 7, 2016
• Publicly Available Date: Dec 7, 2016
• Peer Reviewed: Peer Reviewed
• Keywords: Breast Cancer; Classification methods; Consensus Classification (Classifusion); Consensus Clustering
• Public URL: https://nottingham-repository.worktribe.com/output/827434
• Publisher URL: http://ieeexplore.ieee.org/document/7744150/
• Additional Information: Published in: 2016 IEEE Congress on Evolutionary Computation (CEC) doi:10.1109/CEC.2016.7744150 ISBN 978-1-5090-0622-9.; ; © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.