دانلود مقاله انگلیسی رایگان:یادگیری تقویتی برای زمانبندی بهینه درمان گلیوبلاستوما با تموزولومید - 2020
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  • Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide
    Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide


    ترجمه فارسی عنوان مقاله:

    یادگیری تقویتی برای زمانبندی بهینه درمان گلیوبلاستوما با تموزولومید


    منبع:

    Sciencedirect - Elsevier - Computer Methods and Programs in Biomedicine, 193 (2020) 105443. doi:10.1016/j.cmpb.2020.105443


    نویسنده:

    Amir Ebrahimi Zade a , Seyedhamidreza Shahabi Haghighi a , ∗, Madjid Soltani b , c , d , e


    چکیده انگلیسی:

    Background: : Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients. Methods: : The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule. Results: : Computational results confirm that the optimal chemotherapy schedule depends on some pa- tient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size. Conclusion: : The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.
    Keywords: Glioblastoma multiforme | Treatment scheduling | Reinforcement learning | Multi scale modeling | Temozolomide


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 2206 کیلوبایت

    قیمت: رایگان


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