臺灣博碩士論文加值系統

目的 : 為了分析乳癌患者接受放射治療後，其肺臟劑量與放射性肺炎併發症(radiation pneumonitis, RP) 之反應關係。
材料與方法 : 本研究建立且比較五種不同正常組織併發症機率 (normal tissue complication probability, NTCP) 模型，同時定義出本研究族群所適用之NTCP預測模型。研究對象為87位乳癌患者，去除5筆劑量離散極端值，總共82筆資料進行分析。患者使用強度調控放射治療技術 (intensity-modulated radiotherapy, IMRT) 或混合型放射治療技術 (hybrid intensity-modulated radiotherapy, Hybrid IMRT) 進行治療。患者於放射治療後三個月進行併發症評估，最終併發症定義根據加拿大國家癌症研究所通用毒性標準 (Common Toxicity Criteria - National Cancer Institute of Canada, CTC - NCIC)，主要觀察胸腔電腦斷層掃描 (computed tomography, CT) 影像變化，患者於CT影像中有第一級以上肺炎併發症之影像變化。接著針對樣本資料建立五種NTCP預測模型，分別為LKB (Lyman Kutcher-Burman)、Logistic、Schultheiss、Poisson、Kallman-s模型，NTCP預測模型主要用以探討同側肺臟劑量對於放射性肺炎併發症之反應曲線。針對整體模型效能、區別力與校準能力進行評估檢定，經由模型評估結果比較五種模型預測效力，並定義出適合本研究族群之預測模型。另外，本研究以LKB模型為基礎，建立有效體積LKB - Veff預測模型，觀察體積效應對於劑量與併發症發生率之影響，提供臨床醫師決定處方劑量之參考。
結果 : 本研究所建立五種NTCP模型之參數分別為 (1) LKB模型 : TD50 = 21.42 Gy (95% CI, 20.13 - 22.83), m = 0.27 (95% CI, 0.18 - 0.56)；(2) Logistic模型 : TD50 = 21.41 Gy (95% CI, 20.12 - 22.86), γ= 1.48 (95% CI, 0.71 - 2.35)；(3) Schultheiss模型 : TD50 = 21.26 Gy (95% CI, 19.89 - 22.74), k = 5.65 (95% CI, 2.67 - 9.07)；(4) Poisson模型 : TD50 = 21.21 Gy (95% CI, 19.83 - 22.68), γ= 1.46 (95% CI, 0.74 - 2.21) ；(5) Kallman-s模型 : TD50 = 21.66 Gy (95% CI, 20.25 - 23.15), γ= 1.46 (95% CI, 0.74 - 2.20), s = 1.01。Kallman-s模型之整體模型效能AIC (Akaike's information criterion, AIC) 評估優於其他四種模型，其餘區別力與校準能力等評估參數則五種模型能力相當。
結論 :乳癌患者接受放射治療時，降低肺部接受劑量能夠有效減少肺炎併發症之發生機率，本研究結果最佳模型Kallman-s，定義放射性肺炎50% 併發症機率之劑量值為21.66 Gy，對於肺部器官可運用有效體積概念減少併發症發生，增加乳癌患者放療後生活品質。

Purpose : To investigate the relationship between lung dose and radiation pneumonitis in breast cancer patients after radiotherapy.
Materials and methods : We built and compared five normal tissue complication probability (NTCP) models through outcomes of radiation pneumonitis in breast cancer patients, and defined the best predictive NTCP model for local population in this study.
87 patients with breast cancer were evaluated and 5 outlier samples were excluded. In total, 82 patient data were used in this study. The patients were treated by intensity-modulated radiotherapy or hybrid intensity-modulated radiotherapy techniques. The patients were evaluated by chest computed tomography (CT) at 3 months after completion of radiation therapy. Density changes on chest CT were evaluated by comparing with the CT image prior to radiation therapy for radiation therapy treatment planning. Clinically complication was defined according to the modified Common Toxicity Criteria of the National Cancer Institute (CTC-NCIC). We used the sample data to build five NTCP models. The five models were LKB (Lyman Kutcher-Burman), Logistic, Schultheiss, Poisson and Kallman-s model, respectively. The five NTCP models were compared by different model performance validation tools. We also built LKB - Veff model based on the LKB model. LKB - Veff model provided the correlation of effective volume and dose at the same complication probability for clinical phycisian.
Results : The fitted parameters of five NTCP models were (1) LKB model :
TD50 = 21.42 Gy (95% CI, 20.13 - 22.83), m = 0.27 (95% CI, 0.18 - 0.56)；(2) Logistic model : TD50 = 21.41 Gy (95% CI, 20.12 - 22.86), γ= 1.48 (95% CI, 0.71 - 2.35)；(3) Schultheiss model : TD50 = 21.26 Gy (95% CI, 19.89 - 22.74), k = 5.65 (95% CI, 2.67 - 9.07)；(4) Poisson model : TD50 = 21.21 Gy (95% CI, 19.83 - 22.68), γ= 1.46 (95% CI, 0.74 - 2.21) ；(5) Kallman-s model : TD50 = 21.66 Gy (95% CI, 20.25 - 23.15), γ= 1.46 (95% CI, 0.74 - 2.20), s = 1.01. Overall performance Akaike's Information Criterion (AIC) of Kallman-s model was better than the other four models, but other performance validation were equal in five models.
Conclusions : Reducing lung radiation dose in breast cancer patients can effectively reduce the probability of radiation pneumonitis. The dose of 50% probability of complications of radiation pneumonitis was 21.66 Gy in Kallman-s model, which was the best model in our study. Reducing the effective volume of irradiated lung could improve the quality of life of breast cancer patients.

摘要 i
Abstract iii
致謝 v
目錄 vi
表目錄 viii
圖目錄 ix
符號縮寫 x
符號說明xi
第一章 緒論 1
1.1 動機 1
1.2 目的 2
1.3 相關文獻探討 3
第二章 材料與方法 5
2.1 前言 5
2.2 病患資料 7
2.2.1 極端值分析 9
2.2.2 獨立樣本T檢定 9
2.2.3 放射治療技術 10
2.2.4 最終併發症評估指標 11
2.3 資料降階處理 12
2.3.1 三維劑量分布之電腦斷層掃描影像 13
2.3.2 二維劑量-體積直方圖轉換 14
2.3.3 等效均勻劑量 16
2.3.4 廣義等效均勻劑量 17
2.4 正常組織併發症機率預測模型介紹 18
2.4.1 經驗法則模型說明 18
2.4.1.1 Lyman Kutcher-Burman model 18
2.4.1.2 Logistic model 22
2.4.1.3 Schultheiss model 24
2.4.2 生物法則模型說明 25
2.4.2.1 Poisson model 25
2.4.2.2 Kallman-s model 25
2.5 模型效能評估方法 26
2.5.1 整體模型檢定 (Overall) 26
2.5.1.1 正規化白氏得分 (scale Brier score) 26
2.5.1.2 Akaike's信息準則評估 26
2.5.2 模型區別力檢定 (Discrimination) 27
2.5.2.1 受測者特徵之曲線下面積 27
2.5.2.2 模型準確率 28
2.5.3 模型校準檢定 (Calibration) 28
2.5.3.1 Hosmer-Lemeshow test 28
2.5.3.2 校準曲線 (Calibration curve) 28
第三章 結果 29
3.1 前言 29
3.2 正常組織併發症機率預測模型研究成果 30
3.2.1 經驗法則模型結果 30
3.2.1.1 Lyman Kutcher-Burman model 30
3.2.1.2 Logistic model 31
3.2.1.3 Schultheiss model 33
3.2.2 生物法則模型結果 34
3.2.2.1 Poisson model 34
3.2.2.2 Kallman-s model 35
3.3 有效體積模型 36
3.3.1 劑量體積效應 37
3.3.2 正常組織併發症機率與劑量體積變化之關係 39
3.4 模型效能評估結果 42
3.5.1 整體模型檢定 (Overall) 42
3.5.2 模型區別力檢定 (Discrimination) 42
3.5.3 模型校準檢定 (Calibration) 43
第四章 討論 45
4.1 整體NTCP模型探討 45
4.2 相關研究之乳癌放射性肺炎併發症因子探討 49
4.3 有效體積運用 51
4.4 劑量體積影響關係 52
4.5 Kallman-s 模型建立之發現與探討 53
第五章 結論 54
參考文獻 55
自傳 63

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