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Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice

Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice

Mehrdad J. Gangeh, Hadi Tadayyon, William T. Tran, Gregory Jan Czarnota
Copyright: © 2017 |Pages: 30
ISBN13: 9781522525158|ISBN10: 1522525157|EISBN13: 9781522525165
DOI: 10.4018/978-1-5225-2515-8.ch011
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MLA

Gangeh, Mehrdad J., et al. "Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice." Handbook of Research on Data Science for Effective Healthcare Practice and Administration, edited by Elham Akhond Zadeh Noughabi, et al., IGI Global, 2017, pp. 227-256. https://doi.org/10.4018/978-1-5225-2515-8.ch011

APA

Gangeh, M. J., Tadayyon, H., Tran, W. T., & Czarnota, G. J. (2017). Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice. In E. Noughabi, B. Raahemi, A. Albadvi, & B. Far (Eds.), Handbook of Research on Data Science for Effective Healthcare Practice and Administration (pp. 227-256). IGI Global. https://doi.org/10.4018/978-1-5225-2515-8.ch011

Chicago

Gangeh, Mehrdad J., et al. "Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice." In Handbook of Research on Data Science for Effective Healthcare Practice and Administration, edited by Elham Akhond Zadeh Noughabi, et al., 227-256. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2515-8.ch011

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Abstract

Precision medicine is an emerging medical model based on the customization of medical decisions and treatments to individuals. In personalized cancer therapy, tailored optimal therapies are selected depending on patient response to treatment rather than just using a one-size-fits-all approach. To this end, the field has witnessed significant advances in cancer response monitoring early after the start of therapy administration by using functional medical imaging modalities, particularly quantitative ultrasound (QUS) methods to monitor cell death at microscopic levels. This motivates the design of computer-assisted technologies for cancer therapy assessment, or computer-aided-theragnosis (CAT) systems. This chapter elaborates recent advances in the design and development of CAT systems based on QUS technologies in conjunction with advanced texture analysis and machine learning techniques with the aim of providing a framework for the early assessment of cancer responses that can potentially facilitate switching to more efficacious treatments in refractory patients.

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