Prostate cancer radiogenomics—from imaging to molecular characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radio-logical assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-de-signed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research

Radiomics and imaging genomics in precision medicine

“Radiomics,” a field of study in which high-throughput data is extracted and large amounts of advanced quantitative imaging features are analyzed from medical images, and “imaging genomics,” the field of study of high-throughput methods of associating imaging features with genomic data, has gathered academic interest. However, a radiomics and imaging genomics approach in the oncology world is still in its very early stages and many problems remain to be solved. In this review, we will look through the steps of radiomics and imaging genomics in oncology, specifically addressing potential applications in each organ and focusing on technical issues

Clear Cell Renal Cell Carcinoma 2021–2022

Clear cell renal cell carcinoma is currently one of the most interesting areas of study in oncology. Despite the advances made in this field, this tumor continues to be a health problem of major concern in Western societies, seriously affecting public health services. Several characteristics of this tumor make it an exciting meeting point for translational collaboration between clinicians and basic researchers. Clear cell renal cell carcinoma is a paradigmatic example of inter- and intra-tumor heterogeneity from morphological, immunohistochemical, and molecular viewpoints. This tumor is also a good example to investigate the complexity of tumor/tumor and tumor/environment relationships from an ecological perspective. A deeper identification of the varied internal tumor self-organization through the specialization of cell clones and subclones as local invaders and metastasizers, on one hand, and the interactions of specific subsets of tumor cells with the local host microenvironment, on the other, will significantly enrich our knowledge of this neoplasm. Clear cell renal cell carcinoma is also a paradigmatic test bench for antiangiogenic and immune checkpoint blockage therapies. The refinement of these therapeutic tools administered alone or in combination is a hot issue in oncology, and several international trials are underway

Applications of Artificial Intelligence in Biomedical Sciences

Η παρακάτω διπλωματική εργασία αποτελεί μια εκτεταμένη βιβλιογραφική ανασκόπηση των Εφαρμογών της Τεχνητής Νοημοσύνης στις Βιοϊατρικές Επιστήμες. Πιο αναλυτικά, εστιάζει στις εφαρμογές της Τεχνητής Νοημοσύνης στην Ανάπτυξη Νέων Φαρμάκων, στην Ανάλυση Εικόνων (Image Analysis), στην Ιατρική Φροντίδα (Healthcare), στα Radiomics και στις Κλινικές Δοκιμές (Clinical Trials). Η Τεχνητή Νοημοσύνη έχει αποτελέσει ακρογωνιαίο λίθο στην ανάπτυξη πολλών άλλων επιστημών και σύμφωνα με πλήθος ειδικών και ερευνητών θεωρείτο η μεγαλύτερη ανακάλυψη του αιώνα. Στο πρώτο κεφάλαιο αναλύεται η ιστορία της Τεχνητής Νοημοσύνης καθώς και ο ορισμός αυτής. Στο επόμενο κεφάλαιο γίνεται η αναζήτηση της βιβλιογραφίας στην οποία παρουσιάζονται τα πρώτα βήματα που έγιναν ώστε να δημιουργηθεί η επιστήμη που γνωρίζουμε σήμερα. Έπειτα αναλύονται οι εφαρμογές αυτής σε διάφορους τομείς καθώς και η συμβολή της στην περαιτέρω ανάπτυξη τους. Τέλος, στο τελευταίο κεφάλαιο, κεφάλαιο 4, γίνεται η συζήτηση πάνω σε ό,τι ειπώθηκε προηγουμένως καθώς και προτείνονται νέοι δρόμοι ανάπτυξης της επιστήμης της Τεχνητής Νοημοσύνης.In this dissertation is presented the contribution of AI in biomedical sciences and particularly in drug development, image analysis, healthcare, radiomics and clinical trials. It will be demonstrated the general theoretical context behind the evolution of Artificial Intelligence, as well as its applications. The first chapter, analyzes the history of Artificial Intelligence starting with its definition. The second chapter includes a review of the literature underlining some of the most important milestones of the creation of Artificial Intelligence. As AI has been conducive to the development of many fields it has been characterized by many experts as the biggest innovation of the century. Thus, the third chapter presents the different methods of machine learning used in those fields. In the last chapter of the thesis, chapter 4, is represented a discussion about the findings of the thesis as well as about some new ways that Artificial Intelligence could be beneficial