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
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch093
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MLA

Gangeh, Mehrdad J., et al. "Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1794-1823. https://doi.org/10.4018/978-1-7998-2460-2.ch093

APA

Gangeh, M. J., Tadayyon, H., Tran, W. T., & Czarnota, G. J. (2020). Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1794-1823). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch093

Chicago

Gangeh, Mehrdad J., et al. "Machine Learning Applications in Cancer Therapy Assessment and Implications on Clinical Practice." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1794-1823. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch093

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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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