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.

中文翻译：

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将放射组学整合到整体疗法中进行个性化肿瘤治疗：从算法到床边。

在当前医学影像研究和技术发展中，放射学，人工智能和深度学习已成为最近的流行语。在个性化治疗的评估和随访中对医学大数据的分析也已成为精准医学领域的主要研究主题。在这篇综述中，分析了放射学的当前研究趋势，从手工放射学特征提取和统计分析到深度学习。放射学算法现在包括基因组学和免疫学数据，以改善患者分层并预测治疗反应。几个应用已经显示出决定性的结果，证明了将其他“组学”数据包含到现有成像功能中的潜力。我们还将讨论数据统一和管理基础架构的进一步挑战，以阐明将放射线学和所有其他“组学”急需整合到临床工作流程中的情况。特别是，我们指出了大数据基础架构实施中正在出现的范式转变，以促进数据库的增长，数据提取和专家软件工具的开发。安全地访问，共享和集成所有健康数据（称为“完整组学”），将加速个性化医学和肿瘤学的革命，并扩大成像专家的作用。数据提取和专家软件工具的开发。安全地访问，共享和集成所有健康数据（称为“完整组学”），将加速个性化医学和肿瘤学的革命，并扩大成像专家的作用。数据提取和专家软件工具的开发。安全地访问，共享和集成所有健康数据（称为“完整组学”），将加速个性化医学和肿瘤学的革命，并扩大成像专家的作用。