"Defining the biological and clinical basis of radiomics; towards clinical imaging biomarkers"

Keywords: AI, cancer, precision medicine, big data, machine learning, radiomics, genomics, diagnostic, survival

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.