Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era.Citation formats

Standard

Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era. / Azria, David; West, Catharine; Rosenstein, Barry S.

In: Frontiers in Oncology, Vol. 7, No. 83, 27.04.2017, p. 1-11.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Azria, David ; West, Catharine ; Rosenstein, Barry S. / Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era. In: Frontiers in Oncology. 2017 ; Vol. 7, No. 83. pp. 1-11.

Bibtex

@article{7b3e54aca29440cdbaf8b609bd5768dc,
title = "Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era.",
abstract = "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 = "David Azria and Catharine West and Rosenstein, {Barry S.}",
year = "2017",
month = apr,
day = "27",
doi = "10.3389/fonc.2017.00083",
language = "English",
volume = "7",
pages = "1--11",
journal = "Frontiers in Oncology",
issn = "2234-943X",
publisher = "Frontiers Media S. A.",
number = "83",

}

RIS

TY - JOUR

T1 - Data-Based Radiation Oncology: Design of Clinical Trials in the Toxicity Biomarkers Era.

AU - Azria, David

AU - West, Catharine

AU - Rosenstein, Barry S.

PY - 2017/4/27

Y1 - 2017/4/27

N2 - 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.

AB - 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.

U2 - 10.3389/fonc.2017.00083

DO - 10.3389/fonc.2017.00083

M3 - Article

VL - 7

SP - 1

EP - 11

JO - Frontiers in Oncology

JF - Frontiers in Oncology

SN - 2234-943X

IS - 83

ER -