Towards Personalized Radio-Chemotherapy – Learning from Clinical Data vs. Model Optimization
| Autor: | Jarosław Śmieja, Rafał Suwiński, Krzysztof Fujarewicz, Andrzej Świerniak |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computer science business.industry Optimal treatment Optimal control Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis Research studies Tumor growth Artificial intelligence Sensitivity (control systems) business computer Survival analysis Radio chemotherapy Parametric statistics |
| Zdroj: | Intelligent Information and Database Systems ISBN: 9783030419639 ACIIDS (1) |
| DOI: | 10.1007/978-3-030-41964-6_32 |
| Popis: | We summarize results of our research studies on models of combined anticancer radio- and chemotherapy and their comparison with real clinical data. We use two mathematical techniques, which, to our knowledge, have not been applied simultaneously: optimal control theory and survival analysis. We recall results of analytical optimization of combined chemo-radio-therapy for a simple model of tumor growth with respect to the order, in which these two modes of treatment should be applied. Then we study both structural and parametric sensitivity of this model and related optimal control problem. Afterwards, we present results of survival analysis based on the Kaplan-Meier curves for different protocols of chemo-radio-therapy and compare them with real clinical data and results of optimal treatment protocols. |
| Databáze: | OpenAIRE |
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