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Imaging Biomarkers and Imaging Biobanks

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Artificial Intelligence in Medical Imaging

Abstract

Several challenges exist for the adoption of advanced image analysis methods in clinical routine. Imaging biomarkers not only have to be objective and reproducible, but they also have to show a clear efficacy in the detection and diagnosis of the disease and/or in the evaluation of treatment response. This efficacy must be confirmed by a close relationship with disease hallmarks, which allows them to act as surrogate indicators of relevant clinical outcomes such as the time to treatment response, the progression-free survival, the overall survival, and others. Finally, to achieve clinical integration and to expand its utility, the methodology must be cost-efficient. In this chapter, the general methodology for the development, validation, and implementation of imaging biomarkers is presented. The approach consists of a systematic methodology that allows to achieve a high precision and accuracy in the usage of imaging biomarkers, making it feasible to integrate them in automated pipelines for the generation of massive amounts of radiomic data to be used for storage in imaging biobanks.

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Correspondence to Angel Alberich-Bayarri .

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Alberich-Bayarri, A., Neri, E., MartĂ­-BonmatĂ­, L. (2019). Imaging Biomarkers and Imaging Biobanks. In: Ranschaert, E., Morozov, S., Algra, P. (eds) Artificial Intelligence in Medical Imaging. Springer, Cham. https://doi.org/10.1007/978-3-319-94878-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-94878-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94877-5

  • Online ISBN: 978-3-319-94878-2

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