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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79307完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中明(Chung-Ming Chen) | |
| dc.contributor.author | Ho-Feng Chen | en |
| dc.contributor.author | 陳和豐 | zh_TW |
| dc.date.accessioned | 2022-11-23T08:57:49Z | - |
| dc.date.available | 2022-01-17 | |
| dc.date.available | 2022-11-23T08:57:49Z | - |
| dc.date.copyright | 2022-01-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-11-08 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79307 | - |
| dc.description.abstract | "肺癌已成為世界上最主要癌症死因之一，並且其發病率與死亡率都有逐年上升的趨勢，晚期肺癌患者的5年平均存活率僅有15%。依治療和預後的不同，肺癌主要分為兩種：(I)非小細胞肺癌(Non-small-cell lung cancer，NSCLC)；(II)小細胞肺癌(Small-cell lung cancer，SCLC)。其中有85%的患者是屬於NSCLC，並且NSCLC患者大部分都被診斷為肺腺癌(Lung Adenocarcinoma, LAC)。EGFR(epidermal growth factor receptor)是肺癌治療中最有用的biomarkers之一。在亞洲有高達50%的肺癌患者有表皮生長因子受體基因突變(EGFR mutations, mEGFR)。mEGFR患者對EGFR tyrosine kinase inhibitor (EGFR TKI)的反應優於無mEGFR患者。本研究提出「同時考慮CT影像腫瘤內部patchwise成分」的核心概念，開發一套基於深度學習之肺腺癌mEGFR預測模型。結合CT radiomic特徵與patch-based的腫瘤內部區域資訊尋找分類特徵，以協助LAC患者於標靶治療的治療規劃。本研究預測模型在僅考慮腫瘤區域成分的因素下，找尋腫瘤CT影像中之特徵。為達此目標，首先分為肺區分割以及腫瘤分割。 分割結果顯示，本研究之肺區分割平均Dice coefficient為0.9891；腫瘤分割結果平均Dice coefficient為0.806。 接著從分割的腫瘤中提取了 212個3D 灰度共生矩陣(GLCM)之特徵。通過sequence forward feature selection選到energy和entropy為重要特徵。透過patch-base的方式使用5×5×5立方體大小計算原始影像上energy以及entropy的特徵圖作為RGANN分類模型的第二、三個通道輸入。接著在RGANN的第四層加入gated attention機制，將前一層輸入的特徵圖與分割的腫瘤binary影像相乘去引導 RGANN 模型只關注於腫瘤區域，以提高分類的準確性。同時，RGANN的方法與GANN的方法進行了比較。RGANN 在training cohort （n=591，AUC=0.96，ACC = 0.98）validation cohort（n=85，AUC = 0.83，ACC = 0.81）和testing cohort（n=169，AUC = 0.77，ACC = 0.76) 優於 GANN 模型testing cohort（n=169，AUC = 0.74，ACC = 0.73）。此外，本研究針對lung phantom在9種不同輻射劑量(Tube current)與3種不同重建演算法下進行radiomic特徵的提取，並將研究結果應用於真實病人之Lung CT影像上進行分類。研究顯示，在輻射劑量小於200mA的CT影像提取出的radiomic特徵有較大的變化；反之，提取出的特徵則較穩定。 本研究將蒐集之CT影像分為以上兩種情況進行訓練，並且與原始訓練結果進行比較。分類結果顯示，當CT影像皆為大於200mA的情況下，RGANN得到的測試結果為（n=71，AUC = 0.78，ACC = 0.771）；當CT影像皆為小於200mA的情況下，RGANN得到的測試結果為（n=98，AUC = 0.63，ACC = 0.676）。以上分類結果可見掃描CT影像時，使用不同的Tube current參數會造成擷取的radiomic特徵有不同的變化，導致在分類mEGFR的結果上顯示，使用較高劑量的CT影像進行分析能得到較好的分類結果。 本研究所提出之RGANN模型透過擷取腫瘤內部patchwise成分，在預測mEGFR方面較僅使用原始CT影像的DL模型達到較好的分類結果。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T08:57:49Z (GMT). No. of bitstreams: 1 U0001-2809202110523900.pdf: 4767461 bytes, checksum: 9af23493f4f0bbf963e82d4b7661c2da (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 論文口試委員審定書 2 謝辭 3 摘要 4 Abstract 6 目錄 8 圖目錄 10 表目錄 11 第1章 緒論 12 1.1研究背景與動機 12 1.2研究目的 16 1.3表皮生長因子(EGFR)表型介紹 17 1.3.1 mEGFR主要分布族群與發生位置 18 1.4表皮生長因子(EGFR) mutations之臨床診斷工具 18 1.5表皮生長因子(EGFR) mutations治療藥物 18 1.5.1第一代EGFR-TKI 19 1.5.2第二代EGFR-TKI 19 1.5.3第三代EGFR-TKI 20 第2章 文獻回顧 21 2.1肺區域之半自動/自動分割 21 2.2腫瘤區域之半自動/自動分割 22 2.3表皮生長因子(EGFR)表型之電腦輔助診斷 23 2.3.1 EGFR突變表現型與其預後之文獻回顧 24 2.3.2 EGFR突變與CT影像關聯性之文獻回顧 24 第3章 CNN之基礎理論 27 3.1 Convolutional layer 27 3.2 Pooling layer 28 3.3 Spatial dropout 29 3.4 Flatten and Fully connected layer 30 第4章 研究材料與方法 31 4.1研究硬體設備與資料 31 4.2收案對象 31 4.3研究方法 31 4.3.1 Automatic Lung area segmentation 32 4.3.2 Semi-automatic nodule segmentation 34 4.3.3 Extract patch-based Radiomic feature maps 37 4.3.4 mEGFR classification model 39 4.3.5 Classification model explanation 41 4.3.6 Performance metrics 42 第5章 研究結果與討論 44 5.1肺區域分割結果 44 5.2 EGFR腫瘤區域分割結果 46 5.3 mEGFR分類結果 49 5.3.1使用常規Radiomics特徵進行mEGFR分類模型 49 5.3.2基於深度學習之mEGFR分類模型 51 5.3.3 mEGFR分類之結果與討論 53 5.4 Lung CT影像在不同劑量與重建演算法下之radiomic feature差異 56 5.4.1將CT影像分為兩種不同掃描輻射劑量之訓練結果 60 第6章 結論與未來展望 63 6.1結論 63 6.2未來展望 63 參考文獻 65 | |
| dc.language.iso | zh-TW | |
| dc.title | 基於肺部電腦斷層之肺腺癌EGFR突變預測:結合Patch-based radiomics紋理特徵圖於深度學習網路 | zh_TW |
| dc.title | EGFR mutations prediction of lung adenocarcinoma based on lung computer tomography: Combined with Patch-based radiomics texture feature map in deep learning network | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張允中(Hsin-Tsai Liu),林孟暐(Chih-Yang Tseng),潘亭壽 | |
| dc.subject.keyword | 肺腺癌,表皮生長因子受體,EGFR突變,深度學習,lung phantom,radiomics特徵, | zh_TW |
| dc.subject.keyword | Lung Adenocarcinoma,biomarkers,epidermal growth factor receptor,EGFR mutations,deep learning,lung phantom,radiomics feature, | en |
| dc.relation.page | 70 | |
| dc.identifier.doi | 10.6342/NTU202103420 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-11-09 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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