Department of Radiation Oncology, University of California San Francisco, San Francisco, California.
Medical Physics Unit, McGill University, Montreal, Canada.
Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1074-1082. doi: 10.1016/j.ijrobp.2018.08.032. Epub 2018 Aug 28.
The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.
企业数字化成像的采用，以及定量成像方法的发展和统计学习的重新出现，为通过变革性数据科学研究实现更个性化的癌症治疗开辟了机会。在过去的 5 年中，越来越多的证据表明，无创先进的成像分析（即放射组学）可以揭示肿瘤表型的关键成分，用于治疗过程中多个时间点的多个病变。许多使用自有软件的小组已经从三维医学图像中提取了工程化和深度学习的定量特征，以更好地了解肿瘤生物学的空间和纵向特征，并预测各种结果。这些进展可以增强患者分层和预后判断，支持新兴的靶向治疗方法。不幸的是，这种不成熟的科学学科的快速普及导致了许多早期出版物，这些出版物缺少关键信息或使用的是没有代表性的患者数据集，没有产生可推广的结果。定量成像研究很复杂，应该遵循关键原则才能充分发挥其潜力。定量成像和放射组学领域特别需要重新关注最佳研究设计和报告实践、标准化、可解释性、数据共享和临床试验。为了推动该领域的发展，需要对图像采集、特征计算和统计分析（即机器学习）进行标准化。应该采用开放和多样化参与者（医疗机构、供应商和协会）之间的新数据共享范例，以加快成像生物标志物的开发和全面的临床验证。在对该领域的回顾和批评中，我们提出了工作原则和对当前科学方法的根本改变，目标是进行高影响力的研究和开发可操作的预测模型，从而为精准癌症医学的更有意义的应用提供依据。
