Abstract
Risk assessment and follow-up of oral potentially malignant disorders in patients with mild or moderate oral epithelial dysplasia is an ongoing challenge for improved oral cancer prevention. Part of the challenge is a lack of understanding of how observable features of such dysplasia, gathered as data by clinicians during follow-up, relate to underlying biological processes driving progression. Current research is at an exploratory phase where the precise questions to ask are not known. While traditional statistical and the newer machine learning and artificial intelligence methods are effective in well-defined problem spaces with large datasets, these are not the circumstances we face currently. We argue that the field is in need of exploratory methods that can better integrate clinical and scientific knowledge into analysis to iteratively generate viable hypotheses. In this perspective, we propose that visual analytics presents a set of methods well-suited to these needs. We illustrate how visual analytics excels at generating viable research hypotheses by describing our experiences using visual analytics to explore temporal shifts in the clinical presentation of epithelial dysplasia. Visual analytics complements existing methods and fulfills a critical and at-present neglected need in the formative stages of inquiry we are facing.
Highlights
The lack of understanding of the natural history of oral cancer is a major barrier to our ability to impactfully intervene early in the disease
We propose that visual analytics (VA) is wellsuited to such a niche, providing an approach that can be used to integrate “data-driven” and “knowledge-driven” processes into an iterative analysis that can improve our understanding of the natural history of oral cancer development
We illustrate the value of VA by discussing a simple exploratory visual analysis of lesion shifts in oral dysplasia we conducted using data from the Oral Cancer Prediction Longitudinal (OCPL) study
Summary
The lack of understanding of the natural history of oral cancer is a major barrier to our ability to impactfully intervene early in the disease. A key missing component in this effort is methods that allow us to frame and utilize such complex and heterogeneous data They are highly multi-faceted and demand the integration of diverse clinical and scientific knowledge to generate testable hypotheses informed by the most comprehensive understanding of why lesions shift over time and when such changes may be clinically important. Formative approaches that aim to understand how clinical data might be interrogated, and that support the scientific inductive process of developing, testing, and iterating over a theory are better suited This requires the integration of expert knowledge into analysis. The long-term goal of the OCPL study is to use this cohort, with its diverse data on clinical, histologic, and molecular change, and its samples, to help us answer some of the key management questions for these patients: Which of these dysplastic lesions is at risk for progression? We illustrate the value of VA by discussing a simple exploratory visual analysis of lesion shifts in oral dysplasia we conducted using data from the OCPL study
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