It is now recognized that intratumoral heterogeneity is associated with more aggressive tumor phenotypes leading to poor patient outcomes (1). Medical imaging plays a central role in related investigations, because radiologic images are routinely acquired during cancer management. Imaging modalities such as ¹⁸F-FDG PET, CT, and MRI are minimally invasive and would constitute an immense source of potential data for decoding tumor phenotypes (2). Computer-aided diagnosis methods and systems exploiting medical images have been developed for decades, but their wide clinical implementation has been hampered by false-positive rates (3). As a consequence, routine clinical exploitation of images still consists mostly of visual or manual assessments. Today, the development of machine-learning techniques and the rise of computational power allow for the exploitation of a large number of quantitative features (4). This ability has led to a new incarnation of computer-aided diagnosis, “radiomics,” which refers to the characterization of tumor phenotypes via the extraction of high-dimensional mineable data—for example, morphologic, intensity-based, fractal-based, and textural features—from medical images and whose subsequent analysis aims at supporting clinical decision making.

中文翻译：

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负责任的放射学研究，可加快临床翻译速度

现在已经认识到，肿瘤内异质性与更具侵略性的肿瘤表型有关，导致患者预后不良（1）。医学影像学在相关研究中起着核心作用，因为放射线图像是在癌症治疗期间例行采集的。成像模式，例如¹⁸F-FDG PET，CT和MRI具有微创性，将构成解码肿瘤表型的大量潜在数据来源（2）。利用医学图像的计算机辅助诊断方法和系统已经开发了数十年，但是由于误报率的存在，其广泛的临床应用受到了阻碍（3）。结果，图像的常规临床利用仍然主要包括视觉或人工评估。如今，机器学习技术的发展和计算能力的提高允许利用大量的定量特征（4）。这种能力带来了计算机辅助诊断的新形式，即“放射学”，它是指通过提取高维可挖掘数据（例如形态学，基于强度，基于分形，