During the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined approaches where visual assessment made by the radiologist is augmented by quantitative imaging biomarkers are gaining attention. Advances in machine learning resulted in the rise of radiomics that is a new methodology referring to the extraction of quantitative information from medical images. Radiomics are based on the development of computational models, referred to as “Radiomic Signatures”, trying to address either unmet clinical needs, mostly in the field of oncologic imaging, or to compare radiomics performance with that of radiologists. However, to explore this new technology, initial publications did not consider best practices in the field of machine learning resulting in publications with questionable clinical value. In this paper, our effort was concentrated on how to avoid methodological mistakes and consider critical issues in the workflow of the development of clinically meaningful radiomic signatures.

中文翻译：

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如何为肿瘤患者的临床使用开发有意义的放射组学特征

在过去的十年中，放射学中越来越多地使用定量方法，以努力减少与放射学解释主观方式相关的诊断变异性。由放射科医师进行的视觉评估通过定量成像生物标志物进行增强的组合方法正受到关注。机器学习的进步导致了放射组学的兴起，这是一种新的方法，指的是从医学图像中提取定量信息。放射组学基于计算模型的开发，称为“放射学特征”，试图解决未满足的临床需求，主要是在肿瘤成像领域，或者将放射组学的性能与放射科医生的性能进行比较。然而，为了探索这项新技术，最初的出版物没有考虑机器学习领域的最佳实践，导致出版物的临床价值有问题。在本文中，我们的努力集中在如何避免方法错误并考虑开发具有临床意义的放射组学特征的工作流程中的关键问题。