臺灣博碩士論文加值系統

目的 : 針對肺癌病患接受電腦刀 (CyberKnife M6)之身體立體定位放射治療 (SBRT)技術後誘發二次癌症風險 (SCR)發生率評估。
材料與方法 : 本研究收集20位肺癌病患之回溯性資料，排除年齡為超過70歲以上肺癌病患，使用7位肺癌病患資料均接受SBRT之動態準直儀 (IRIS)技術治療進行評估，透過兩種常見二次癌症模型分別為BEIR VII (Biological Effects of Ionizing Radiation)與Schneider模型評估，因BEIR VII與Schneider兩者模型定義不同，Schneider模型需經過生物等效劑量 (BED)轉換並透過公式建立風險等效劑量 (RED)模型，根據病患接受單位劑量計算器官等效劑量 (OED)，評估不同OED模型時考慮病患年齡與性別因素計算額外絕對風險 (EAR)與可歸因終生危險度 (LAR)，進行評估脊椎、正常肺臟及整體肺臟等誘發癌症之危急器官 (OARs)與誘發肉瘤之計劃靶區體積 (PTV)誘發二次癌症風險值以利於臨床規劃參考。
結果 : 誘發癌症型態脊椎器官EAR、LAR之線性 (Lnt)、指數 (LinExp)、高原 (Plateau)、混合 (Full model)模型對於BED轉換前後在統計學上均無顯著差異，EAR差值分別為2.63%、5.08%、5.17%及6.19%，LAR差值分別為4.58%、6.27%、6.43%及6.65%。正常肺臟與整體肺臟之線性模型皆有較高二次癌症風險，在於BED轉換前後四種模型在統計學上均有顯著差異，正常肺臟之EAR差值分別為-18.56%、17.78%、11.53%及9.76%，LAR差值分別為 -20.53%、19.38%、12.08%及10.17%，整體肺臟之EAR差值分別為 -19.62%、18.30%、11.69%及9.84%，LAR差值分別為-21.63%、20.02%、12.23%及10.30%。可能誘發肉瘤之PTV線性模型皆有較高二次癌症風險，對於BED轉換前後四種模型在統計學均有顯著差異，EAR差值分別為
-36.44%、-30.30%、0.98%及13.29%，LAR差值分別為-36.54%、-30.05%、1.55%及12.47%。
結論 : 本研究發現Schneider與BEIR VII線性模型評估EAR風險值，需注意計算BEIR VII模型可能會低估5-13倍。誘發癌症之危急器官結果表示四種模型皆可依線性模型評估結果為主，應注意在低劑量區域可能會潛在誘發二次癌症風險，而誘發肉瘤之PTV結果則是取決於不同細胞修復程度，其接受劑量程度和暴露年齡皆會影響病患之二次癌症風險。

Purpose: To induce secondary cancer risks (SCR) assessment following CyberKnife M6 stereotactic body radiation therapy (SBRT) for lung cancer patients.
Materials and methods: This study collected retrospective data from 20 lung cancer patients, excluding patients with lung cancer who are over age 70, to evaluate 7 lung cancer patients who were treated with SBRT VariableAperture Collimator (IRIS) technology by two common secondary cancer models were Biological Effects of Ionizing Radiation (BEIR VII) and Schneider models. Because BEIR VII and Schneider models are different, the Schneider model required biologically effective dose (BED) conversion and created risk equivalent dose (RED) through the formula, organ equivalent dose (OED) were calculated according to the patient's unit dose-response, excess absolute risk (EAR) and lifetime attributable risk (LAR) were calculated by gender and age factors when evaluated different OED models, evaluated SCR values of organ at risks (OARs) including Spinal cord, Normal lung and Total lung in carcinoma and planning target volume (PTV) in sarcoma for clinical planning reference.
Results: Spinal cord in carcinoma was found that linear, LinExp, Plateau and Full models of EAR and LAR were not statistically significant differences between before and after the BED conversion, EAR difference was 2.63%, 5.08%, 5.17%, and 6.19%, LAR difference was 4.58%, 6.27%, 6.43%, and 6.65%, respectively. The linear models of Normal lung and Total lung had higher secondary cancer risks, four models were statistically significant differences between before and after the BED conversion, EAR difference of Normal lung was -18.56%, 17.78%, 11.53%, and 9.76%, LAR difference was -20.53%, 19.38%, 12.08%, and 10.17%, respectively. EAR difference of Total lung was -19.62%, 18.30%, 11.69%, and 9.84%, LAR difference was -21.63%, 20.02%, 12.23%, and 10.30%, respectively. The linear model of PTV in Sarcoma had higher secondary cancer risks, four models were statistically significant differences between before and after the BED conversion, EAR difference was -36.44%, -30.30%, 0.98%, and 13.29%, LAR difference was -36.54%, -30.05%, 1.55%, and 12.47%.
Conclusions: In this study, we found that linear models of Schneider and BEIR VII assessed EAR values, and it should be noted that the calculation of the BEIR VII model may be underestimated by 5-13 multiples. The results of OARs in carcinoma showed that four models could be based on linear model assessment results, and it should be noted that the risks of secondary cancer may be potentially induced in the low-dose area, the results of sarcoma model were determined by the degree of cell repair, levels of radiation dose and exposure age also affected the patient's secondary cancer risks.

摘要 i
Abstract iii
致謝 v
目錄 vi
表目錄 viii
圖目錄 ix
符號縮寫 xi
符號說明 xii
第一章 緒論 1
1.1 動機 1
1.2 目的 4
1.3 相關文獻探討 5
第二章 材料與方法 7
2.1 前言 7
2.2 病患資料 9
2.3 電腦刀 (CyberKnife M6)放射治療計劃 10
2.4 二次癌症預測分析 15
2.4.1 BEIR VII模型二次癌症分析 16
2.4.2 生物等效劑量 (BED)轉換 16
2.4.3 Schneider模型風險等效劑量 (RED) 17
2.4.3.1 誘發Carcinoma之風險等效劑量 (RED)反應模型 18
2.4.3.2 線性劑量反應模型 19
2.4.3.3 指數劑量反應模型 20
2.4.3.4 高原劑量反應模型 21
2.4.3.5 混和劑量反應模型 22
2.4.3.6 誘發Sarcoma之風險等效劑量 (RED)反應模型 23
2.4.3.7 線性劑量反應模型 24
2.4.3.8 低修復劑量反應模型 25
2.4.3.9 中修復劑量反應模型 26
2.4.3.10 完全修復劑量反應模型 27
2.4.4 器官等效劑量 (OED) 28
2.4.5 額外絕對風險 (EAR) 29
2.4.6 可歸因之終生危險度 (LAR) 30
第三章 結果 31
3.1 前言 31
3.2 BEIR VII線性模型評估 32
3.3 生物等效劑量 (BED)轉換 32
3.4 劑量轉換體積直方圖 33
3.5 Schneider模型二次癌症評估 35
3.5.1 誘發Carcinoma之器官等效劑量模型 35
3.5.2 誘發Sarcoma之器官等效劑量模型 39
3.5.3 誘發Carcinoma之額外絕對風險模型 40
3.5.4 誘發Sarcoma之額外絕對風險模型 45
3.5.5 誘發Carcinoma之可歸因終生危險度模型 46
3.5.6 誘發Sarcoma之可歸因終生危險度模型 50
第四章 討論 52
4.1 Schneider與BEIR VII比較 52
4.2誘發Carcinoma器官之四種模型對於劑量轉換之影響 54
4.3誘發Sarcoma器官之四種模型對於劑量轉換之影響 56
4.4誘發二次癌症對於年齡之影響 57
第五章 結論 61
參考文獻 62
自傳 71

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