کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6915766 1447408 2017 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data
ترجمه فارسی عنوان
انتخاب و اعتبار مدل پیش بینی اثرات تابش بر رشد تومور بر اساس داده های تصویربرداری غیر تهاجمی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
The Occam Plausibility Algorithm (OPAL) is implemented to provide a Bayesian statistical calibration of the model classes, 39 models in all, as well as the determination of the most plausible models in these classes relative to the observational data, and to assess model inadequacy through statistical validation processes. Discussions of the numerical analysis of finite element approximations of the system of stochastic, nonlinear partial differential equations characterizing the model classes, as well as the sampling algorithms for Monte Carlo and Markov chain Monte Carlo (MCMC) methods employed in solving the forward stochastic problem, and in computing posterior distributions of parameters and model plausibilities are provided. The results of the analyses described suggest that the general framework developed can provide a useful approach for predicting tumor growth and the effects of radiation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 327, 1 December 2017, Pages 277-305
نویسندگان
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