Dekker explored radiomics, particularly in non-small-cell lung cancer imaging, addressing the challenges of extracting quantitative features from medical images to develop diagnostic, prognostic, or predictive models integrating biological and medical data. Collaborating with a team of researchers, he conducted a radiomic analysis of 1,019 patients with lung or head-and-neck cancer using computed tomography (CT) imaging data, identifying prognostic radiomic features that showed significant power in independent datasets of the cancer patients. Additionally, in describing the concept and potential of radiomics in cancer research, he and his collaborators proposed guidelines to enhance the scientific integrity and clinical relevance of radiomics investigations. Furthermore, his systematic review assessed the reproducibility of radiomic features used in cancer imaging for clinical decision making, noting that feature stability is influenced by factors such as image acquisition settings, reconstruction algorithms, and preprocessing methods.