Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement

Abstract : The optimal design and patient selection for interventional trials in radiogenomics seem trivial at first sight. However, radiogenomics do not give binary information like in e.g. targetable mutation biomarkers. Here, the risk to develop severe side effects is continuous, with increasing incidences of side effects with higher doses and/or volumes. In addition, a multi-SNP assay will produce a predicted probability of developing side effects and will require one or more cut-off thresholds for classifying risk into discrete categories. A classical biomarker trial design is therefore not optimal, whereas a risk factor stratification approach is more appropriate. Patient selection is crucial and this should be based on the dose-response relations for a specific endpoint. Alternatives to standard treatment should be available and this should take into account the preferences of patients. This will be discussed in detail.
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https://hal.umontpellier.fr/hal-02295648
Contributeur : Anthony Herrada <>
Soumis le : mardi 24 septembre 2019 - 13:16:39
Dernière modification le : mercredi 25 septembre 2019 - 01:23:38

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Dirk de Ruysscher, Gilles Defraene, Bram L.T. Ramaekers, Philippe Lambin, Erik Briers, et al.. Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement. Radiotherapy and Oncology, Elsevier, 2016, 121 (3), pp.440-446. ⟨10.1016/j.radonc.2016.11.003⟩. ⟨hal-02295648⟩

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