Keywords: radiomics, precision oncology, clinical decision support system (CDSS), artificial intelligence

"Quantitative Imaging and Artificial Intelligence in Oncology"

Cancer is the second most fatal disease worldwide. Cancer treatment is a complex process and conventional treatment often fails in many patients due to heterogeneity in the patient population. The advent of biomarkers has facilitated the development of personalized treatment in oncology. Medical imaging is an integral part of cancer management and since the last decade, medical images have also been identified for quantitative analysis to develop imaging biomarkers (radiomic features). The radiomic feature extraction from medical images has led to data explosion, which is the source of BIG imaging data in oncology. Artificial intelligence (AI) algorithms like machine learning (ML) and deep learning (DL) have been applied to Big data imaging in order to develop decision support systems (DSS) in precision oncology. The radiomic community has also identified the key issues related to the implementation of radiomics-based DSS: (a) robustness of radiomic features, (b) development and implementation of AI infrastructure in hospitals, (c) multicentre and prospective radiomics studies, (d) creating awareness and faith among doctors and patients. This thesis has addressed most of the issues to facilitate the implementation of radiomics-based DSS in clinical practice.      

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Language: English