Big Data-Based Decision Support Systems for Hadron Therapy

Abstract

In the last decade, major advances have been made in the field of radiation oncology, bringing new diagnostic techniques and expanding the number of treatment modalities. Traditional evidence-based medicine uses randomised trials that are designed to represent homogenous populations of patients, and are not based upon patient, disease and treatment parameters. The human cognitive capacity is limited, however, making predictive modelling and big data in radiation oncology an increasingly essential tool in decision-making. This chapter discusses the process of gathering data, training models and developing DSS using rapid learning health care (RLHC). This is especially important when considering treatments such as hadron therapy. Modern technologies such as intensity-modulated radiotherapy (IMRT), brachytherapy (BT), volumetric arc radiotherapy (VMAT) or particle-beam therapy, such as hadron therapy, allow for localised dose delivery around the target volume with maximum sparing of the organs at risk with very high dosimetric accuracy.

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