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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
| dc.contributor.author | Sin-You Chang | en |
| dc.contributor.author | 張馨友 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:24:00Z | - |
| dc.date.available | 2021-09-17 | |
| dc.date.available | 2022-11-24T03:24:00Z | - |
| dc.date.copyright | 2021-09-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-08 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80966 | - |
| dc.description.abstract | "肺癌是全球死亡率最高的癌症，五年存活率僅約一至兩成，準確的預後評估可輔助醫生及早做出個人化醫療的相關決策並有效降低死亡率。一般而言，癌症分期是預後最重要的指標之一，但即使患者處於相同的癌症分期，存活時間也大為不同。因此，需要探索額外的預後因子以提升預測的準確度。 本研究提出了一種結合胸腔電腦斷層掃描影像(Computed tomography, CT)與臨床數據的預後系統來預測肺癌患者的生存狀態。首先，從CT影像擷取出腫瘤區域並輸入至卷積神經網路(Convolutional neural network, CNN)中進行切割以取得腫瘤遮罩，該模型由修改過的三維HarDNet-MSEG網路與瓶頸注意力模塊(Bottleneck attention module, BAM)所構成。接著，利用腫瘤區域影像與腫瘤遮罩提取影像特徵，包含影像組學(Radiomics)與CNN特徵，前者用於計算腫瘤的形狀與紋理屬性，後者利用ResNeSt模型自動學習存活預測的圖像特徵。為消除冗餘資訊，採用一種基於多個機器學習模型的特徵選擇策略來保留最具鑑別度的特徵。最後，使用全連接神經網路整合臨床數據、影像組學與CNN特徵以預測患者的存歿狀態。本研究在三年預後與五年預後實驗中，分別使用437筆與302筆肺癌案例來評估預後系統的性能。根據結果，提出的方法在三年預後中，其準確度、靈敏度、特異性和ROC曲線下面積(AUC)分別為80.78%、73.57%、84.18%和0.8425，而五年預後為81.79%、86.67%、74.59%和0.8373，其性能優於使用單一種特徵與多種機器學習分類器，證實了本研究設計的系統可透過結合圖像特徵與臨床指標有效提升肺癌的預後性能。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:24:00Z (GMT). No. of bitstreams: 1 U0001-0709202123200600.pdf: 2556851 bytes, checksum: 608fd8323ecf7feb90f29043c79a68c2 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 摘要 iii Abstract iv Table of Contents vi List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Materials 5 Chapter 3 Methods 9 3.1 Image Preprocessing 10 3.2 Tumor Segmentation 11 3.2.1 3-D HarDNet-MSEG 13 3.2.2 Bottleneck Attention Module (BAM) 15 3.2.3 Loss Function 17 3.3 Image Feature Extraction 18 3.3.1 Tumor Region Radiomics Features 19 3.3.2 CNN-based Features 22 3.4 Multivariate Data Analysis 24 3.4.1 Clinical Data Preprocessing 24 3.4.2 Holistic Feature Selection 25 3.5 Prognosis 27 3.6 Experimental Setting 28 Chapter 4 Experimental Results 30 4.1 Environment 30 4.2 Evaluation and Statistics 30 4.3 Comparison of Different Models for Segmentation 31 4.4 Comparison of Different Combinations of Feature Types 32 4.5 Comparison of Different Classifiers 38 4.6 Analysis of Selected Features 44 Chapter 5 Discussion and Conclusion 50 References 56 | |
| dc.language.iso | en | |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 肺癌 | zh_TW |
| dc.subject | 預後 | zh_TW |
| dc.subject | 電腦斷層掃描 | zh_TW |
| dc.subject | 影像組學 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 特徵選擇 | zh_TW |
| dc.subject | Feature selection | en |
| dc.subject | Machine learning | en |
| dc.subject | Lung cancer | en |
| dc.subject | Prognosis | en |
| dc.subject | Computed tomography | en |
| dc.subject | Radiomics | en |
| dc.subject | Convolution neural network | en |
| dc.title | 使用AI驅動的肺癌預後模型整合臨床數據與定量影像組學於胸腔電腦斷層掃描 | zh_TW |
| dc.title | AI-driven Prognostic Modeling for Lung Cancer Using Clinical Data and Quantitative Radiomic Features from Chest CT Images | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 羅崇銘(Hsin-Tsai Liu),陳啟禎(Chih-Yang Tseng) | |
| dc.subject.keyword | 肺癌,預後,電腦斷層掃描,影像組學,卷積神經網路,特徵選擇,機器學習, | zh_TW |
| dc.subject.keyword | Lung cancer,Prognosis,Computed tomography,Radiomics,Convolution neural network,Feature selection,Machine learning, | en |
| dc.relation.page | 61 | |
| dc.identifier.doi | 10.6342/NTU202103047 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-09 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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