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Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era. Front Oncol 2017;7:83



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Scopus ID

2-s2.0-85019032512 (requires institutional sign-in at Scopus site)   29 Citations


The ability to stratify patients using a set of biomarkers, which predict that toxicity risk would allow for radiotherapy (RT) modulation and serve as a valuable tool for precision medicine and personalized RT. For patients presenting with tumors with a low risk of recurrence, modifying RT schedules to avoid toxicity would be clinically advantageous. Indeed, for the patient at low risk of developing radiation-associated toxicity, use of a hypofractionated protocol could be proposed leading to treatment time reduction and a cost-utility advantage. Conversely, for patients predicted to be at high risk for toxicity, either a more conformal form or a new technique of RT, or a multidisciplinary approach employing surgery could be included in the trial design to avoid or mitigate RT when the potential toxicity risk may be higher than the risk of disease recurrence. In addition, for patients at high risk of recurrence and low risk of toxicity, dose escalation, such as a greater boost dose, or irradiation field extensions could be considered to improve local control without severe toxicities, providing enhanced clinical benefit. In cases of high risk of toxicity, tumor control should be prioritized. In this review, toxicity biomarkers with sufficient evidence for clinical testing are presented. In addition, clinical trial designs and predictive models are described for different clinical situations.

Author List

Azria D, Lapierre A, Gourgou S, De Ruysscher D, Colinge J, Lambin P, Brengues M, Ward T, Bentzen SM, Thierens H, Rancati T, Talbot CJ, Vega A, Kerns SL, Andreassen CN, Chang-Claude J, West CML, Gill CM, Rosenstein BS


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