Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island.
National Center for Health Promotion and Disease Prevention, Veterans Health Administration, Durham, North Carolina.
JAMA Oncol. 2020 Aug 1;6(8):1282-1286. doi: 10.1001/jamaoncol.2020.1247.
There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems.
To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology.
The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology.
Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics.
Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.
从单个癌症病例中可以获得大量且不断增长的数据，这使得临床肿瘤学家的工作更加繁重。这一数据挑战吸引了工程师们开发旨在改善癌症诊断或治疗的软件。然而，将计算机用于肿瘤学临床中的诊断或治疗已经导致计算预测系统过早或不恰当地使用。
评估开发和评估预测计算方法在肿瘤学中的临床实用性的最佳实践。
国家癌症政策论坛和国家科学院、工程院和医学研究所的数理科学和分析委员会举办了一次研讨会，以研究来自癌症患者的多维数据的使用以及用于分析这些数据的计算方法。研讨会召集了不同的利益攸关方和专家，包括计算机科学家、肿瘤临床医生、统计学家、患者权益倡导者、行业领导者、伦理学家、学术和社区为基础的卫生系统的领导者、私营和公共健康保险公司、联邦机构和监管机构。在三个主题领域中考虑了成功的计算肿瘤学的关键特征：（1）数据质量、完整性、共享和隐私；（2）用于分析、解释和使用肿瘤学数据的计算方法；（3）最佳使用计算肿瘤学精准度的临床基础设施和专业知识。
从数据收集到数据处理、管理和使用，质量控制被发现是所有阶段的关键。在每次癌症诊断和重新分期时收集标准化的简约数据集，可以增强临床数据的可靠性和完整性，用于精准肿瘤学。数据完整性是指癌症诊断、治疗和结果等关键数据元素的信息，而数据质量取决于是否以有效和可靠的方式测量了适当的变量。从不同人群中收集数据可以降低创建无效和有偏差的算法的风险。辅助临床医生的计算系统应被归类为医疗器械软件，因此应根据潜在风险进行监管。为了促进对肿瘤学中高维数据进行解释的计算方法的适当使用，治疗医生需要获得具有广泛专业知识和深厚培训的多学科团队的支持，这些团队是临床肿瘤学住院医师中的一部分，专注于临床信息学。
研讨会讨论提出了展示预测计算方法在诊断或治疗癌症方面的临床实用性的最佳实践。
