@article{b72a008774b641cf8648bfa884397351,
title = "Forecasting Individual Patient Response to Radiation Therapy in Head and Neck Cancer With a Dynamic Carrying Capacity Model",
abstract = "Purpose: To model and predict individual patient responses to radiation therapy. Methods and Materials: We modeled tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the effect of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for 2 independent cohorts of patients with head and neck cancer from the H. Lee Moffitt Cancer Center (MCC) and the MD Anderson Cancer Center (MDACC) who received 66 to 70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiation therapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes. Results: The model fit data from MCC with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from MDACC with comparable accuracy using the tumor growth rate learned from the MCC cohort, showing transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of 4 on-treatment tumor volume measurements. Conclusions: These results demonstrate that our simple mathematical model can describe a variety of tumor volume dynamics. Furthermore, combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.",
author = "Zahid, {Mohammad U.} and Nuverah Mohsin and Mohamed, {Abdallah S.R.} and Caudell, {Jimmy J.} and Harrison, {Louis B.} and Fuller, {Clifton D.} and Moros, {Eduardo G.} and Heiko Enderling",
note = "Funding Information: This research has been supported in part by funding and salary support to H.E. from the National Institutes of Health (NIH), including grant U01CA244100 from the National Cancer Institute (NCI). Additionally, this research has been supported in part by funding and salary support to C.D.F. from the NIH, including grant R01DE028290 from the National Institute for Dental and Craniofacial Research Academic Industrial Partnership; grant 1R01CA218148 from the NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program; an NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award (P30CA016672) from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program; and an NIH/NCI Head and Neck Specialized Programs of Research Excellence Developmental Research Program Award (P50 CA097007). Direct infrastructure support was provided by the multidisciplinary Stiefel Oropharyngeal Research Fund of the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer and the Cancer Center Support Grant (P30CA016672) and the MD Anderson Program in Image-guided Cancer Therapy. Funding Information: Disclosures: M.U.Z. and H.E. are inventors on a provisional patent application titled Personalized Radiation Therapy. J.J.C. has received research grant support and honoraria from and has consulted for Varian Medical Systems. L.B.H. is the principal investigator of a ViewRay research grant. C.D.F. received funding and salary support unrelated to this project from the National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award, from the National Science Foundation (NSF), Division of Mathematical Sciences, Joint National Institutes of Health (NIH)/NSF Initiative on Quantitative Approaches to Biomedical Big Data, and from Elekta AB; grants from the NSF Division of Civil, Mechanical, and Manufacturing Innovation, the National Institute of Biomedical Imaging and Bioengineering Research Education Programs for Residents and Clinical Fellows, and the MD Anderson Sister Institution Network Fund; and the NIH Big Data to Knowledge Program of the National Cancer Institute Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award. H.E. receives funding and salary support unrelated to this project from an NIH/NCI grant. No other disclosures were reported. Publisher Copyright: {\textcopyright} 2021 The Authors",
year = "2021",
month = nov,
day = "1",
doi = "10.1016/j.ijrobp.2021.05.132",
language = "English (US)",
volume = "111",
pages = "693--704",
journal = "International Journal of Radiation Oncology Biology Physics",
issn = "0360-3016",
publisher = "Elsevier Inc.",
number = "3",
}