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Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement. Radiother Oncol 2016 Dec;121(3):440-446



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Scopus ID

2-s2.0-85006399698 (requires institutional sign-in at Scopus site)   14 Citations


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.

Author List

De Ruysscher D, Defraene G, Ramaekers BLT, Lambin P, Briers E, Stobart H, Ward T, Bentzen SM, Van Staa T, Azria D, Rosenstein B, Kerns S, West C


Sarah L. Kerns PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Biomedical Research
Genetic Markers
Genetic Predisposition to Disease
Patient Selection
Radiation Injuries
Radiotherapy Dosage
Research Design
Risk Assessment