Remote self-sampling may improve diagnosis and prognosis while reducing costs, but numerous factors must be considered for appropriate implementation
Kimia Sobhani, Jennifer Van Eyk | | Opinion
The precision diagnostic tests that often inform essential treatment decisions can take years to develop and validate – and even then their applicability is often limited to specific indications. Companion diagnostics, for example, are useful for predicting safety- and efficacy-related responses to treatment, but may be considered niche in their window of application. Broader diagnostics that harness the utility of previously validated, well-studied circulating biomarkers can be used to address disease-specific diagnostic and prognostic questions, both in and out of the clinic.
Continuous or semi-continuous biomarker assessment relies on our existing knowledge of changing biomarker concentrations over time and/or in response to disease development. For many clinically established analytes, however, the expected shift in concentration over time is poorly defined, especially in early disease. These days, artificial intelligence can be applied to help pinpoint disease risk and progression from routine lab tests, and – in some cases – routine monitoring can enhance the power of these approaches.
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