Computer methods and programs in biomedicine · 2025
Malignant melanoma fractional-order mathematical model with stabilized fuzzy sliding mode control.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Amilo David, Sadri Khadijeh, Hincal Evren
Аннотация
BACKGROUND AND OBJECTIVE: Malignant melanoma, an aggressive form of skin cancer, poses significant challenges due to its rapid progression, metastatic potential, and resistance to therapies. This study aims to develop a fractional-order mathematical model capturing melanoma dynamics (tumor-immune interactions, extracellular matrix remodeling, nutrient dynamics) and introduce a Stabilized Fuzzy Sliding Mode Control (SFSMC) strategy to suppress tumor growth and restore microenvironmental homeostasis.
METHODS: A fractional-order model was derived using Caputo derivatives to incorporate memory effects and long-range dependencies. The SFSMC combines sliding mode control with fuzzy logic to manage uncertainties. Theoretical analysis included well-posedness, stability (via Lyapunov functions), and computation of the reproduction number R0. Numerical simulations were performed using a predictor-corrector method with parameters calibrated from clinical data.
RESULTS: The model demonstrated stability when R0<1, indicating tumor suppression. SFSMC reduced tumor cell populations by 78% and circulating tumor cells by 65% while improving immune response (45% increase in immune cells) and nutrient availability (30% recovery). Sensitivity analysis revealed R0 is mostly influenced by tumor growth rate, natural degradation rate of extracellular matrix (ECM), rate of ECM degradation by tumor cells, and ECM production rate, suggesting their potential role in suppressing tumor growth.
CONCLUSIONS: The fractional-order framework and SFSMC offer a robust approach to modeling and controlling melanoma, with potential clinical implications for adaptive therapy.
METHODS: A fractional-order model was derived using Caputo derivatives to incorporate memory effects and long-range dependencies. The SFSMC combines sliding mode control with fuzzy logic to manage uncertainties. Theoretical analysis included well-posedness, stability (via Lyapunov functions), and computation of the reproduction number R0. Numerical simulations were performed using a predictor-corrector method with parameters calibrated from clinical data.
RESULTS: The model demonstrated stability when R0<1, indicating tumor suppression. SFSMC reduced tumor cell populations by 78% and circulating tumor cells by 65% while improving immune response (45% increase in immune cells) and nutrient availability (30% recovery). Sensitivity analysis revealed R0 is mostly influenced by tumor growth rate, natural degradation rate of extracellular matrix (ECM), rate of ECM degradation by tumor cells, and ECM production rate, suggesting their potential role in suppressing tumor growth.
CONCLUSIONS: The fractional-order framework and SFSMC offer a robust approach to modeling and controlling melanoma, with potential clinical implications for adaptive therapy.