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Machine learning methods for predicting tumor response in lung cancer

Authors


  • This work was supported in part by CIHR-MOP-114910 and Fast Foundation grants.

Abstract

Among cancer victims, lung cancer accounts for most fatalities in men and women. Patients at advanced stages of lung cancer suffer from poor survival rate. Majority of these patients are not candidates for surgery and receive radiation therapy (radiotherapy) as their main course of treatment. Despite effectiveness of radiotherapy against many cancers, more than half of these patients are unfortunately expected to fail. Recent advances in biotechnology have allowed for an unprecedented ability to investigate the role of gene regulation in lung cancer development and progression. However, limited studies have provided insight into lung tumor response to radiotherapy. The inherent complexity and heterogeneity of biological response to radiation therapy may explain the inability of existing prediction models to achieve the necessary sensitivity and specificity for clinical practice's or trial's design. In this study, we briefly review the current knowledge of genetic and signaling pathways in modulating tumor response to radiotherapy in non-small cell lung cancer as a case study of data mining application in the challenging cancer treatment problem. We highlight the role that data mining approaches, particularly machine learning methods, can play to improve our understanding of complex systems such as tumor response to radiotherapy. This can potentially result in identification of new prognostic biomarkers or molecular targets to improve response to treatment leading to better personalization of patients' treatment planning by reducing the risk of complications or supporting therapy that is more intensive for those patients likely to benefit. © 2012 Wiley Periodicals, Inc.

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