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研究生:鍾銘芮
研究生(外文):Ming Jui Chung
論文名稱:[氟-18]去氧葡萄糖正子放射斷層影像之半定量測量對預測T4NxM0期鼻咽癌患者治療預後的分析
論文名稱(外文):The investigation of prognosis prediction in T4 nasopharyngeal cancer with the semiquantitative measurements of FDG PET images
指導教授:閻紫宸閻紫宸引用關係蕭穎聰
指導教授(外文):T. C. YenI. T. Hsiao
學位類別:碩士
校院名稱:長庚大學
系所名稱:醫學物理暨影像科學研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
論文頁數:92
中文關鍵詞:鼻咽癌全病灶醣解率預後因子復發轉移存活率
外文關鍵詞:nasopharyngeal cancertotal lesion glycolysisprognosis predictorrecurrencedistant metastasissurvival rate
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本研究主要在於回顧性地(retrospectively)分析治療前RT定義的GTV,以及[氟-18]去氧葡萄糖正子放射斷層影像(18F-FDG PET)之新型半定量參數SUVmax、SUVmean、全病灶醣解率(total lesion glycolysis, TLG),在2001年12月到2006年3月間71位罹T4NxM0期鼻咽癌患者治療預後的統計效力,平均治療後追蹤約41.6±20個月(2~79個月),伴隨其他臨床因子個別針對整體存活率(overall survival, OS)、無復發存活率(relapse free survival, RFS)、原位局部無復發存活率(local-regional relapse free survival, LRRFS)、原位控制失敗存活率(local failure free survival, LFFS)、局部控制失敗存活率(regional failure free survival, RFFS)、無遠端轉移存活率(distant relapse free survival, DRFS)、癌病特異存活率(disease specific survival, DSS)等經ROC(receiver operating characteristic)曲線取得各定量參數之最佳閾值(the optimal cutoff value),共同以單變量(univariate analysis)及多變量分析(multivariate analysis)整理出具獨立顯著統計意義的預後因子(prognostic factors)。
結果發現5年OS為71.8%(最佳閾值為SUVmean=4.26 g/ml;HR=4.11, p=0.012),5年RFS為66.2%(TLG=348.32 SUV.ml;HR=3.26, p=0.004),5年LRRFS為76.1%(GTV=59.27 ml;HR=6.20, p=0.001),5年LFFS為78.9%(TLG=348.32 SUV.ml;HR=5.46, p=0.002),5年RFFS為90.1%(GTV=62.28 ml;HR=10.78, p=0.028),5年DRFS為81.7%(僅TLG=598.29 SUV.ml些微顯著;HR=3.17, p=0.055),5年DSS為85.9%(TLG=348.32 SUV.ml;HR=21.82, p=0.003),顯示TLG在治療前預測患者治療後癌症復發或轉移等預後,相對於SUV確實是較具評估價值的,未來仍需要進一步研究TLG對他種癌症預後評估的表現,期能協助制定更精準適切的治療計畫,提高癌症患者的生活品質及存活比率。
The aim of this study mainly lay in retrospectively investigating pre-treatment defined parameters for prognosis prediction in 71 T4NxM0 nasopharyngeal cancer(NPC) patients, who received treatment in Linkou Chang Gung Memorial Hospital between December 2001 and March 2006. Those parameters include GTV and the semiquantitative parameters, such as SUVmax, SUVmean, and total lesion glycolysis(TLG), measured from the initial scanning of 18F-FDG PET before treatment. After 41.6±20 months (2~79 mo) mean duration of follow-up after treatment, we combined those parameters with other clinical risk factors to analyze the overall survival rate(OS), relapse free survival rate(RFS), local-regional relapse free survival rate(LRRFS), local failure free survival rate(LFFS), regional failure free survival rate(RFFS), distant relapse free survival rate(DRFS), disease specific survival rate(DSS). Survival was estimated by the Kaplan-Meier method with log-rank test as the univariate analysis for each parameters with the optimal cutoff respectively derived from receiver operating characteristic(ROC) curve of each survival endpoint. The multivariate analysis using the Cox’s proportional hazards models was made to identify the independent prognosticator with the statistical significance.
The result discovered that 5 year OS is 71.8% (the optimal cutoff is SUVmean=4.26 g/ml; HR=4.11, p=0.012), 5 year RFS is 66.2% (TLG=348.32 SUV.ml; HR=3.26, p=0.004), 5 year LRRFS is 76.1% (GTV=59.27 ml; HR=6.20, p=0.001), 5 year LFFS is 78.9% (TLG=348.32 SUV.ml; HR=5.46, p=0.002), 5 year RFFS is 90.1% (GTV=62.28 ml; HR=10.78, p=0.028), 5 year DRFS is 81.7% (only TLG=598.29 SUV.ml has the slightly statistical significance; HR=3.17, p=0.055), 5 year DSS is 85.9% (TLG=348.32 SUV.ml; HR=21.82, p=0.003). TLG provided superior prognostic information compared to traditional risk factors in predicting DSS, RFS, and LFFS rates, which demonstrated that TLG may be more valuable than SUV for predicting the prognosis outcome of the patient before they treated by a certain treatment. To improve patient's life quality and survival rate, it truly still need to further study the performance of TLG in other cancer types in the future, to assist the oncologists work out more appropriate treatment planning.
Chapter 1. Introduction………………………………………………..1
1.1. Motivation………………………………………………………..1
1.2. Background and Literature review……………………………….2
1.2.1. Nasopharyngeal carcinoma……………………………………………….2
1.2.2. Role of 18F-FDG PET…………………………………………………...12
1.2.3. SUVs measurement……………………………………………………...17
1.2.4. SUVs as treatment response evaluator for NPC………..……………….19
1.2.5. SUVs as prognosis predicator for NPC…………………………………20
1.2.6. The new potential prognosis predicator—TLG…………………………21
1.2.7. Why this study choose T4NxM0 NPC as the objects? …………………23
1.2.8. Study purposes and design………………………………………………24

Chapter 2. materials and methods…………………………………..26
2.1. Patient population………………………………………………26
2.2. Treatment and patients follow-up………………………………26
2.3. 18F-FDG PET imaging protocol………………………………...27
2.4. 18F-FDG PET image analysis…………………………………...28
2.5. Statistical analysis………………………………………………30

Chapter 3 Results …….………………………………………………31
3.1. Statistical power for prognosis prediction of these parameters...31
3.2. The relationship between utput TLG, SUVmean, and GTV...........33

Chapter 4 Discussions………………………………………………...34
Chapter 5 Conclusions………………………………………………..38
Appendix………………….…………………………………………...59 References……………………………………………………….…….69
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