本文關鍵詞： 腫瘤 放射治療 并發(fā)癥 NTCP模型　出處：《東南大學學報(自然科學版)》2015年02期 　論文類型：期刊論文

【摘要】：為了建立具有群體特異性的腫瘤放療NTCP預測模型,提出了一種模型參數(shù)擬合方法.首先,基于NTCP模型的特點構建最大似然函數(shù);然后,分別采用確定性優(yōu)化方法和隨機性優(yōu)化方法對最大似然函數(shù)進行優(yōu)化,分析優(yōu)化過程的時間成本及優(yōu)化結果,探討用于擬合NTCP模型參數(shù)的最優(yōu)方法.實驗結果表明,用于擬合NTCP模型參數(shù)的最大似然函數(shù)是非凸的,存在局部最優(yōu)解;遺傳算法是一種最穩(wěn)定的最大似然函數(shù)優(yōu)化方法,其運行時間比模擬退火算法短,而且可以在每次優(yōu)化結束后給出全局最優(yōu)解,以作為NTCP模型參數(shù).所提方法可以幫助腫瘤放療工作者在臨床隨訪數(shù)據(jù)的基礎上建立具有群體特異性的放療并發(fā)癥預測模型.
[Abstract]:In order to establish a population-specific NTCP prediction model for tumor radiotherapy, a model parameter fitting method is proposed. Firstly, the maximum likelihood function is constructed based on the characteristics of NTCP model, and then the maximum likelihood function is constructed. The deterministic optimization method and stochastic optimization method are used to optimize the maximum likelihood function respectively. The time cost and optimization results of the optimization process are analyzed, and the optimal method for fitting the parameters of the NTCP model is discussed. The experimental results show that, The maximum likelihood function used to fit the parameters of NTCP model is nonconvex and has a local optimal solution. Genetic algorithm is the most stable maximum likelihood function optimization method, and its running time is shorter than that of simulated annealing algorithm. After each optimization, the global optimal solution can be given as a parameter of NTCP model. The proposed method can help tumor radiotherapy workers to establish a group specific prediction model of radiotherapy complications on the basis of clinical follow-up data.
【作者單位】： 東南大學影像科學與技術實驗室;山東省腫瘤防治研究院;雷恩第一大學信號與圖像實驗室;
【基金】：國家自然科學基金資助項目(61271312,81272501,81301298) 國家重點基礎研究發(fā)展計劃(973計劃)資助項目(2011CB707904)
【分類號】：R730.55

【參考文獻】

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本文編號：1512117