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Title: 運用正弦組合技術於膀胱癌放射治療效果之預測
Utilizing the sine function in radiation treatment of bladder cancer prediction
Authors: 林才煜
LIN,TSAI-YU
Contributors: 資訊管理系碩士班
蔡東亦
Keywords: 小樣本;類神經網路;放射線治療;膀胱癌
small sample size;neural networks;radiation treatment;bladder cancer
Date: 2011
Issue Date: 2011-12-01 13:33:48 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理系碩士班]
Abstract: 利用放射線治療膀胱癌可能保有膀胱的正常功能,因此如何選擇膀胱癌的患者,將扮演著重要的角色。據了解,一些內在基因的因素會影響對於膀胱癌在放射線治療之成效。盡管如此,一個或兩個特定基因可能不足預測放射治療的成效,因此分析多種的致癌基因跟抑癌基因,來達到更好的預測。不過,研究人員還沒有一個有效的技術來評估放射線治療,而然要從病人抽取出基因樣本數是非常有限的。因此在本研究提出一種改良的大趨勢擴散技術以及正弦函數來解決小樣本問題。
本研究採用了類神經網路獲得數據資料,數據的呈現出膀胱癌的殘留率。這項建議的預測模組可以幫助病患來決定適不適合在膀胱癌上做放射治療的基礎數據。
Radiotherapy may preserve normal bladder function, and therefore plays an increasingly important role in treatment for selected patients with bladder cancer. It is known that some biological protein factors influence radiation response to bladder cancer. Nevertheless, one or two specific proteins may not be sufficient to predict the effect of radiotherapy, and analyzing multiple oncoproteins and tumor suppressor proteins may achieve better predictions. However, researchers do not yet have an effective technique to evaluate the outcome of radiotherapy on multiple proteins using a very limited number of samples from patients. A modified Mega-Trend-Diffusion technique is proposed to solve the small dataset problem.
This research used the neural network to obtain data, the data showing a residual rate of bladder cancer. The proposed prediction model can help patients to decide if it is appropriate to do radiation therapy in bladder cancer.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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