Personalised Analytic Oncology, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
University of Applied Sciences and Arts Western Switzerland (HES-SO), Sierre, Switzerland.
Eur Radiol Exp. 2020 Feb 7;4(1):11. doi: 10.1186/s41747-019-0143-0.
Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up of personalised treatments has also become a major research topic in the area of precision medicine. In this review, current research trends in radiomics are analysed, from handcrafted radiomics feature extraction and statistical analysis to deep learning. Radiomics algorithms now include genomics and immunomics data to improve patient stratification and prediction of treatment response. Several applications have already shown conclusive results demonstrating the potential of including other "omics" data to existing imaging features. We also discuss further challenges of data harmonisation and management infrastructure to shed a light on the much-needed integration of radiomics and all other "omics" into clinical workflows. In particular, we point to the emerging paradigm shift in the implementation of big data infrastructures to facilitate databanks growth, data extraction and the development of expert software tools. Secured access, sharing, and integration of all health data, called "holomics", will accelerate the revolution of personalised medicine and oncology as well as expand the role of imaging specialists.
放射组学、人工智能和深度学习是当前医学影像研究和技术发展中的热门词汇。在精准医学领域，利用医疗大数据评估和监测个体化治疗的后续效果也成为主要研究课题。本文对放射组学的研究趋势进行了分析，内容涵盖从手工提取放射组学特征到统计分析，再到深度学习。目前，放射组学算法已纳入基因组学和免疫组学数据，以改善患者分层和治疗反应预测。已有多项应用研究得出了明确的结论，证明了将其他“组学”数据纳入现有影像特征的潜力。我们还探讨了数据协调和管理基础设施方面的进一步挑战，以阐明放射组学和所有其他“组学”纳入临床工作流程的迫切需求。我们特别指出，在实施大数据基础设施方面正在出现范式转变，以促进数据库的增长、数据提取以及专家软件工具的开发。安全访问、共享和整合所有健康数据（称为“holomics”）将加速个性化医疗和肿瘤学的革命，并扩大影像专家的作用。
