Beyin tümörü tedavisinde aralıklı kemoterapi planlaması için optimize edilmiş bulanık mantık kontrolör tasarımı

Öz

Bu çalışmada beyin tümörleri için kemoterapi dozajını optimize etmek ve bu yolla tümör popülasyonunun yok edilmesi ile bağışıklık sisteminin korunması arasındaki kritik dengeyi korumak amacıyla tip-2 bulanık mantık kontrolör (T2BMK) önerilmiştir. Parametre optimizasyonu için yapay sinek kuşu algoritması (AHA) ve parçacık sürü optimizasyonu (PSO) kullanılan T2BMK’nın performansı tip-1 bulanık mantık kontrolör (T1BMK) ile karşılaştırılarak değerlendirilmiştir. Çalışmanın temel bir özelliği, klinik gerçekliği artırmak için sürekli ilaç infüzyonundan farklı olarak dinlenme sürelerini de içeren üç tedavi protokolünün uygulanmasıdır. Matematiksel tümör büyüme modeline dayanan benzetim sonuçları, AHA ile optimize edilmiş T2BMK (AHA-T2BMK)’nın vücutta daha az ilaç birikimiyle toksisiteyi azaltıp daha hızlı tümör eliminasyonu sağladığını, sağlıklı hücre ve bağışıklık hücresi popülasyonlarını koruduğunu göstermektedir. Bu bulgular AHA-T2BMK içeren kontrol sisteminin kanser tedavisi gibi karmaşık biyomedikal bir sürece katkıda bulunma potansiyelini ortaya koymaktadır.

Anahtar Kelimeler

• Beyin tümörü
• Kemoterapi
• Tip-2 bulanık mantık kontrolör
• Tedavi protokolü
• Optimizasyon

Optimized fuzzy logic controller design for intermittent chemotherapy scheduling in brain tumor treatment

Abstract

In this study, a type-2 fuzzy logic controller (T2FLC) is proposed to optimize chemotherapy dosage for brain tumors and thereby maintain the critical balance between eliminating the tumor population and preserving the immune system. The performance of the T2FLC, which uses the artificial hummingbird algorithm (AHA) and particle swarm optimization (PSO) for parameter optimization, was evaluated by comparing it with the type-1 fuzzy logic controller (T1FLC). A key feature of the study is the application of three treatment protocols that include rest periods, unlike continuous drug infusion, to increase clinical realism. Simulation results based on a mathematical tumor growth model show that the AHA-optimized T2FLC (AHA-T2FLC) reduces toxicity with less drug accumulation in the body, enables faster tumor elimination, and preserves healthy cell and immune cell populations. These findings highlight the potential of the control system incorporating AHA-T2FLC to contribute to complex biomedical processes such as cancer treatment.

Keywords

• Brain tumor
• Chemotherapy
• Type-2 fuzzy logic controller
• Treatment protocol
• Optimization

Kaynakça

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