Open Access

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

  • Authors:
    • Eleftherios Trivizakis
    • Georgios Z. Papadakis
    • Ioannis Souglakos
    • Nikolaos Papanikolaou
    • Lefteris Koumakis
    • Demetrios A. Spandidos
    • Aristidis Tsatsakis
    • Apostolos H. Karantanas
    • Kostas Marias
  • View Affiliations

  • Published online on: May 11, 2020     https://doi.org/10.3892/ijo.2020.5063
  • Pages: 43-53
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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.

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Journal Cover

July 2020
Volume 57 Issue 1

Print ISSN: 1019-6439
Online ISSN:1791-2423

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APA
Trivizakis, E., Papadakis, G.Z., Souglakos, I., Papanikolaou, N., Koumakis, L., Spandidos, D.A. ... Marias, K. (2020). Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review). International Journal of Oncology, 57, 43-53. https://doi.org/10.3892/ijo.2020.5063
MLA
Trivizakis, E., Papadakis, G. Z., Souglakos, I., Papanikolaou, N., Koumakis, L., Spandidos, D. A., Tsatsakis, A., Karantanas, A. H., Marias, K."Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)". International Journal of Oncology 57.1 (2020): 43-53.
Chicago
Trivizakis, E., Papadakis, G. Z., Souglakos, I., Papanikolaou, N., Koumakis, L., Spandidos, D. A., Tsatsakis, A., Karantanas, A. H., Marias, K."Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)". International Journal of Oncology 57, no. 1 (2020): 43-53. https://doi.org/10.3892/ijo.2020.5063