What are critical elements for the responsible use of computational methods in aiding the diagnosis of cancer and directing treatment of oncology patients?
This workshop-based review evaluates best practices for identifying the following focus areas for enabling responsible use of computational methods in the oncology clinic: data quality, data diversity, risk-based software as a medical device regulatory approval pathway, computational reproducibility, face validity, prospective clinical utility trials, training of clinical oncologists, multidisciplinary boards, and requiring computational methods to recertify clinical oncologists.
Key policy components described in this review may guide the development, regulatory approval of, and use of computers that aid in diagnosing cancer and selecting antineoplastic treatments.
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.
Conclusions and Relevance
Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.
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Panagiotou OA, Högg LH, Hricak H, et al. Clinical Application of Computational Methods in Precision Oncology: A Review. JAMA Oncol. Published online May 14, 2020. doi:10.1001/jamaoncol.2020.1247
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