期刊
JOURNAL OF PERSONALIZED MEDICINE
卷 11, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/jpm11111124
关键词
radiotherapy; dose personalization; head and neck cancer; mathematical modeling
资金
- National Institutes of Health (NIH)
- National Institute for Dental and Craniofacial Research (NIDCR) Academic Industrial Partnership Grant [R01DE028290]
- National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program [1R01CA218148]
- UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program [P30CA016672]
- NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award [P50 CA097007]
Standard radiotherapy doses are designed for general populations, leading to potential dosage discrepancies for individual patients. Personalized radiotherapy doses based on tumor volume dynamics can improve locoregional control, showing potential for both de-escalation and escalation strategies.
Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66-70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics-a dynamics-adapted radiotherapy dose (D-DARD). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66-70 Gy RT in 2-2.12 Gy weekday fractions. This trial was repeated constraining D-DARD between (54, 82) Gy to test more moderate dose adjustment. D-DARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by > 10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates.
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