Abstract
Predictive and prognostic models hold great potential to support clinical decision making in oncology and could ultimately facilitate a paradigm shift to a more personalised form of treatment. While a large number of models relevant to the field of oncology have been developed, few have been translated into clinical use and assessment of clinical utility is not currently considered a routine part of model development. In this narrative review of the clinical evaluation of prediction models in oncology, we propose a high-level process diagram for the life cycle of a clinical model, encompassing model commissioning, clinical implementation and ongoing quality assurance, which aims to bridge the gap between model development and clinical implementation.
Highlights
Oncology ranks among the most complex disciplines of modern medicine
Predictive and prognostic models hold great potential to support clinical decision making in oncology and could facilitate a paradigm shift to a more personalised form of treatment
A major advantage of these models is that they can be developed using real-world data generated by patients treated in routine clinical practice, thereby providing a new form of evidence that is more inclusive of the patient groups commonly under-represented in traditional clinical trials
Summary
Oncology ranks among the most complex disciplines of modern medicine. The inherent heterogeneity of cancer, patients and the ever-expanding number of treatment options make the selection of optimal treatment regimens more challenging than ever. The Predict breast tool is an example of a CPM enriched by causal reasoning, as it uses population average outcomes from clinical trials to provide survival estimates for different treatment combinations [6]. Reported advantages from the clinician perspective include supplementing their existing clinical knowledge, with the more accurate prediction they provide enhancing decision making confidence and potentially improving patient outcomes [22e25].
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