دورية أكاديمية
أعرض في EDS

Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.

التفاصيل البيبلوغرافية
العنوان: Mathematical modeling of radiotherapy: impact of model selection on estimating minimum radiation dose for tumor control.
المؤلفون: Kutuva AR; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.; Department of Microbiology and Cell Science, University of Florida, Gainesville, FL, United States., Caudell JJ; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States., Yamoah K; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States., Enderling H; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.; Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States., Zahid MU; Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.
المصدر: Frontiers in oncology [Front Oncol] 2023 Oct 09; Vol. 13, pp. 1130966. Date of Electronic Publication: 2023 Oct 09 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101568867 Publication Model: eCollection Cited Medium: Print ISSN: 2234-943X (Print) Linking ISSN: 2234943X NLM ISO Abbreviation: Front Oncol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Lausanne : Frontiers Research Foundation]
مستخلص: Introduction: Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.
Methods: In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.
Results: Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.
Discussion: Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Kutuva, Caudell, Yamoah, Enderling and Zahid.)
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معلومات مُعتمدة: R21 CA263911 United States CA NCI NIH HHS; U01 CA244100 United States CA NCI NIH HHS
فهرسة مساهمة: Keywords: mathematical modeling; model comparison; oncology; personalized oncology; radiotherapy
تواريخ الأحداث: Date Created: 20231030 Latest Revision: 20240210
رمز التحديث: 20240210
مُعرف محوري في PubMed: PMC10600389
DOI: 10.3389/fonc.2023.1130966
PMID: 37901317
قاعدة البيانات: MEDLINE
الوصف
تدمد:2234-943X
DOI:10.3389/fonc.2023.1130966