| Literature DB >> 32467997 |
Eleftherios Trivizakis1, Georgios Z Papadakis2, Ioannis Souglakos3, Nikolaos Papanikolaou2, Lefteris Koumakis2, Demetrios A Spandidos4, Aristidis Tsatsakis5, Apostolos H Karantanas2, Kostas Marias2.
Abstract
The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision‑support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed.Entities:
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Year: 2020 PMID： 32467997 PMCID： PMC7252460 DOI： 10.3892/ijo.2020.5063
Source DB: PubMed Journal: Int J Oncol ISSN： 1019-6439 Impact factor: 5.650