臺灣博碩士論文加值系統

目的 : 評估肝細胞癌 (Hepatocellular Carcinoma, HCC)病患接受放射治療 (Radiation Therapy, RT)後誘發肝臟毒性併發症 (Radiation - Induced Hepatic Toxicity, RIHT)之劑量 - 體積參數因子 (Dose - Volume Factors)分析。
材料與方法 : 本研究為回溯性利用收集2014 - 2017年間114位接受使用強度調控放射治療 (Intensity Modulation Radiation Therapy, IMRT)肝細胞癌 (HCC)病患，其中排除肝臟器官接收劑量 - 體積未達到700cc與極端值之病患並利用重複抽樣估計法 (Bootstrapping)增加一倍樣本數達到138筆。根據病患療程期間抽血數值評估上升程度使用常見不良事件評價標準表 (Common Terminology Criteria For Adverse Events, CTCAE)判定有無發生放射治療 (RT)後誘發肝臟毒性併發症 (RIHT)。採用病患正常肝臟接收劑量 - 體積 (Normal Liver Dose - Volume Receiving)和整體肝臟接收劑量 - 體積 (Total Liver Dose - Volume Receiving)以及使用萃取演算法：最小絕對壓縮挑選運算子 (Least Absolute Shrinkage and Selection Operator, LASSO)和貝氏網路因子強度挑選 (Bayesian Network, BN)預測因子，並建立效能演算法模型：邏輯斯迴歸 (Logistic Regression, LR)與單純貝葉斯 (Naive Bayes, NB)最後利用受試者操作特性曲線下面積 (The Area Under the receiver operating characteristic Curve, AUC)、準確度 (Accuracy, ACC)和陰性預測值 (Negative predictive value, NPV)比較模型相關差異性。
結果 : 針對在病患經過放射治療 (RT)誘發肝臟毒性併發症 (RIHT)預測風險劑量 - 體積參數因子在最小絕對壓縮挑選運算子 (LASSO)萃取於邏輯斯迴歸 (LR)建立模型比較結果準確度 (ACC)、受試者操作特性曲線下面積 (AUC)和陰性預測值 (NPV)為正常肝臟接收體積30 Gy (Normal Liver Volume Receiving 30 Gy, NLV30 Gy)，0.76、0.65、0.72與整體肝臟接收體積30 Gy (Total Liver Volume Receiving 30 Gy, TLV30 Gy)，0.84、0.72、0.72，在貝氏網路因子強度挑選 (BN)萃取於單純貝葉斯 (NB)建立模型為整體肝臟接收體積45 & 20 Gy (Total Liver Volume Receiving 45 & 20 Gy, TLV45 & 20 Gy)，0.72、0.76、0.73。
結論 : 本研究結果顯示對於無法切除肝細胞癌 (HCC)患者接受放射治療 (RT)後避免誘發肝臟毒性併發症 (RIHT)之產生應控制在：正常肝臟接受體積V30 Gy (NLV30 Gy)、整體肝臟接受體積V30 Gy (TLV30 Gy)及整體肝臟接受體積V20 Gy (TLV20 Gy)劑量 - 體積參數因子 (Dose - Volume Factors)，並達到肝臟器官對於輻射低耐受度限制與提供醫師做為治療規劃上之參考，及修正合適之治療計畫的指標，希望能藉此達到預防誘發肝臟毒性併發症 (RIHT)之風險。

Purpose : To assess the dose-volume factors analysis of radiation - induced hepatic toxicity (RIHT) in patients with hepatocellular carcinoma (HCC) after radiotherapy.
Materials and methods : The study is retrospectively collected between 2014 and 2017, 114 patients with HCC underwent intensity modulation radiation therapy (IMRT). Of those, liver dose - volume receiving of less than 700 cc and extreme value excluded with remain only 69 patients and to use the bootstrapping of reached 138 samples. Assess blood draw rises level during the course of the patient's treatment RIHT was scored using the Common Terminology Criteria for Adverse Events (CTCAE). The predictor's analysis, we use patient’s dose - volume factors the may cause RIHT, And two kinds of selection algorithm by least absolute shrinkage and selection operator (LASSO), bayesian network (BN) to select the risk predictors. Furthermore, the classification models of the selected risk predictors, all risk predictors were established two kinds of algorithms by logistic regression (LR), naive bayes (NB). Finally, uses the area under the receiver operating characteristic curve (AUC), accuracy (ACC) and negative predictive value (NPV) was compared.
Results : The patients predicted risk dose-volume factors of classification models with AUC, ACC and NPV. First, LASSO select on LR models：normal liver volume receiving 30 Gy (NLV30), 0.76, 0.65, 0.72. Second LASSO select on LR models：total liver volume receiving 30 Gy (TLV30), 0.84, 0.72, 0.72. Third BN select on NB models：total liver volume receiving 45 & 20 Gy (TLV45&20), 0.72, 0.76, 0.73.
Conclusions : The results of this study showed should be limited to the NLV30 Gy, TLV30 Gy, TLV20 Gy as risk predictors of RIHT after IMRT for unresectable HCC, and could reach liver organ with low tolerance for radiation. The advantage of selection factors can be provided to physicians for better decision-making.

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 xii
第一章 緒論 1
1.1 動機 1
1.2 目的 3
1.3 相關文獻探討 4
第二章 材料與方法 6
2.1 前言 6
2.2 病患資料 7
2.3 併發症評估 8
2.4 放射治療技術 10
2.5 候選因子 10
2.5.1 候選因子定義 11
2.6萃取因子演算法 12
2.6.1 最小絕對壓縮挑選運算子 12
2.6.1.1 最小絕對壓縮挑選運算子挑選之步驟 13
2.6.2 貝氏網路因子強度挑選 14
2.6.2.1 貝氏網路因子強度挑選之步驟 15
2.7 模型效能演算法 16
2.7.1 邏輯斯回歸模型 16
2.7.2 單純貝葉斯模型 17
2.8 模型效能評估 18
2.8.1 準確率 18
2.8.2 受試者操作曲線下面積 18
2.8.3 陰性預測值 18
2.9 正常組織併發症模型 19
第三章 結果 20
3.1 前言 20
3.2 樣本平均劑量圖 21
3.3 樣本因子相關性 22
3.4 因子萃取結果 24
3.4.1 最小絕對壓縮挑選運算子挑選之結果 24
3.4.2 貝氏網路因子強度挑選之結果 26
3.5 因子預測模型 29
3.5.1 邏輯斯回歸模型之效能 29
3.5.2 單純貝葉斯模型之效能 30
3.5.3 模型係數與勝算比 31
3.6 正常組織併發症模型 32
第四章 討論 34
4.1 前言 34
4.2 演算法效能驗證 35
4.2.1 最小絕對壓縮挑選運算子之驗證 35
4.2.2 貝氏網路因子強度挑選之驗證 36
4.2.3 邏輯斯回歸與單純貝葉斯模型之比較 38
4.3 治療計畫指標 40
4.3.1 正常肝臟器官治療指標 41
4.3.2 整體肝臟器官治療指標 44
4.4 正常組織併發症模型建立 47
第五章 結論 50
參考文獻 51
自傳 56

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