Thenault Ronan, Gasmi Anis, Khene Zine-Edine, Bensalah Karim, Mathieu Romain
Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou.
IRSET, Rennes, France.
Curr Opin Urol. 2021 Jul 1;31(4):424-429. doi: 10.1097/MOU.0000000000000902.
Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management.
Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice.
In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.
放射基因组学,即放射组学与基因组学的融合,是一个旨在提升对癌症理解与评估的新研究领域。在本综述中,我们概述了放射基因组学及其在前列腺癌管理中的最新相关应用。
关于前列腺癌放射基因组学的文献在过去5年中出现,但仍然稀少。前列腺癌的放射基因组学主要依赖基于MRI的特征。一些成像生物标志物,大多基于深度学习研究中放射组学特征的识别,已被用于预测基因组图谱,如PTEN、Decipher Oncotype DX或Prolaris表达。然而,尽管结果令人鼓舞,但一些局限性仍然阻碍了放射基因组学在日常实践中的任何整合。
未来,随着放射组学和基因组学数据的使用增加,人工智能在泌尿外科的出现可能使放射基因组学在前列腺癌评估中发挥越来越大的作用,在个性化医学领域采用非侵入性和个性化方法。将这种有前景的方法整合到前列腺癌决策中还需要进一步努力。