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Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement
- Source :
- Radiotherapy and Oncology, 121(3), 440-446. Elsevier Ireland Ltd, Radiotherapy and Oncology, Radiotherapy and Oncology, Elsevier, 2016, 121 (3), pp.440-446. ⟨10.1016/j.radonc.2016.11.003⟩
- Publication Year :
- 2016
-
Abstract
- International audience; 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.
- Subjects :
- 0301 basic medicine
Research design
Pathology
MESH: Radiotherapy
Biomedical Research
Radiogenomics
MESH: Genetic Markers
MESH: Risk Assessment
0302 clinical medicine
Neoplasms
MESH: Neoplasms
MESH: Radiotherapy Dosage
Standard treatment
MESH: Genomics
MESH: Research Design
MESH: Genetic Predisposition to Disease
Radiotherapy Dosage
Hematology
Genomics
Risk factor (computing)
3. Good health
Oncology
Research Design
030220 oncology & carcinogenesis
Biomarker (medicine)
Risk assessment
Trial design
Genetic Markers
medicine.medical_specialty
MESH: Radiation Injuries
Risk Assessment
Article
03 medical and health sciences
medicine
Humans
Radiology, Nuclear Medicine and imaging
Medical physics
MESH: Patient Selection
Genetic Predisposition to Disease
Radiation Injuries
Selection (genetic algorithm)
Statement (computer science)
MESH: Humans
Radiotherapy
business.industry
MESH: Biomedical Research
Patient Selection
030104 developmental biology
business
[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Biomarkers
Subjects
Details
- ISSN :
- 18790887 and 01678140
- Volume :
- 121
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
- Accession number :
- edsair.doi.dedup.....51372971fafcc4b3772ea1fc5ce8120e