Artificial Intelligence is on the verge of gaining a seminal role in advanced medicine. This brings new hope for modern cancer care, where success of treatment often depends on identifying targeted therapies that match a patient’s disease profile. In this dissertation, for the first time radiographic, biological, and clinical data were integrated to develop intelligent models that can predict which patients will respond better to a specific therapy. Based on the results of this dissertation, in the future doctors could use the proposed machine learning approach to analyse patients in a way that allows selection of optimal treatment strategies.
|Award date||8 Mar 2018|
|Place of Publication||Maastricht|
|Publication status||Published - 2018|
- precision medicine
- big data
- machine learning
Grossmann, P. B. H. J. (2018). Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. Datawyse / Universitaire Pers Maastricht. https://doi.org/10.26481/dis.20180308pg