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Imaging-Based Prediction Models

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Imaging and Interventional Radiology for Radiation Oncology

Part of the book series: Medical Radiology ((Med Radiol Diagn Imaging))

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Abstract

The modern personalized medicine approaches in oncology deal with a huge and barely manageable number of patient-specific variables, from genetic data to simple blood tests, trying to ensure a fully individualized and tailored therapy.

In this context, the recent research trends towards imaging biomarkers, quantitative imaging analysis and radiomics applications have gained an ever-increasing interest, leading to the proposal of imaging-based predictors for clinical decision support systems (DSS).

The application of advanced machine learning solutions to manage always larger databases of patient imaging-derived variables is becoming increasingly necessary and opens new frontiers in the field of clinical outcome prediction.

The different types of predictive modelling techniques, together with their strengths and pitfalls, are highlighted in this chapter which offers a brief overview about the state of the art as well as on the future developments of this fascinating topic.

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Authors and Affiliations

  1. Radiation Oncology Unit, Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Rome, Italy

    Luca Boldrini

  2. Knowledge Based Oncology (KBO) Laboratories—Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy

    Luca Boldrini & Carlotta Masciocchi

  3. Nuclear Medicine Unit, Fondazione Policlinico Universitario “Agostino Gemelli”, IRCCS, Rome, Italy

    Lucia Leccisotti

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  1. Luca Boldrini
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  2. Carlotta Masciocchi
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  3. Lucia Leccisotti
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Editor information

Editors and Affiliations

  1. Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Regina G.H. Beets-Tan

  2. Humanitas University and Humanitas Clinical and Research Center, Milan, Italy; Department of Radiology and Nuclear Medicine, Rijnstate Hospital, Arnhem, The Netherlands

    Wim J. G. Oyen

  3. Department of Radiology, Radiation Oncology and Hematology Fondazione Policlinico Universitario A.Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy

    Vincenzo Valentini

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Boldrini, L., Masciocchi, C., Leccisotti, L. (2020). Imaging-Based Prediction Models. In: Beets-Tan, R., Oyen, W., Valentini, V. (eds) Imaging and Interventional Radiology for Radiation Oncology. Medical Radiology(). Springer, Cham. https://doi.org/10.1007/978-3-030-38261-2_20

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