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Modeling of Tumor Control Probability (TCP)

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Machine Learning in Radiation Oncology

Abstract

Modeling of tumor control probability is an important task for predicting response in radiotherapy. Most early methods have focused on using biophysical analysis based on understanding irradiation effects from in vitro cell culture. However, it has been recognized that clinical tumor response is multifactorial and involves a complex interaction of physical, biological, and clinical surrogates that data-driven approaches such as machine-learning algorithms would play a prominent role. In this chapter, we present using different examples the process of applying machine learning to modeling TCP and demonstrate its efficacy compared to existing methods and its potential to improving our understanding of tumor response.

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Author information

Authors and Affiliations

  1. Department of Oncology, McGill University, Montreal, QC, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, University of Michigan, Ann Arbor, USA

    Issam El Naqa

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  1. Issam El Naqa
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Correspondence to Issam El Naqa .

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Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

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© 2015 Springer International Publishing Switzerland

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El Naqa, I. (2015). Modeling of Tumor Control Probability (TCP). In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-18305-3_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18304-6

  • Online ISBN: 978-3-319-18305-3

  • eBook Packages: MedicineMedicine (R0)

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