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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82078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 | Li-Wei Chen | en |
| dc.contributor.author | 陳力維 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:35:24Z | - |
| dc.date.available | 2026-11-01 | |
| dc.date.copyright | 2022-01-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-11-01 | |
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Matching and homogenizing convolution kernels for quantitative studies in computed tomography. Investigative radiology 2019;54(5):288. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82078 | - |
| dc.description.abstract | 自2011年起，國際肺癌研究協會、美國胸科學會和歐洲呼吸學會建立了新的肺腺癌亞型分類系統；據研究報導，新亞型具有重要的預後價值，特別是對於浸潤性腺癌 (Invasive Adenocarcinoma，IA)已被表明會影響手術的預後結果。因此，術前 IA 亞型的診斷對於最佳化手術規劃具有重要的價值。然而，常規侵入性的IA亞型斷診被認為是不准確的，另一方面，即使組織學證據表明 IA 亞型與電腦斷層掃描 (Computed Tomography，CT) 特徵之間存在相關性，基於 CT 的 IA 亞型診斷仍然存在三個主要挑戰，包括：(i) ADC 的腫瘤內組織學異質性，(ii) IA 亞型之間不同的 CT 外觀，以及 (iii) 用於深度學習 (Deep Learning，DL) 模型訓練的有限醫學影像樣本量。為了克服這些困難並達到術前基於 CT 的 IA亞型預測，本論文實施了兩種開發途徑，包括利用“hand-craft-features”和基於 DL 的放射組學方法。 對於基於“hand-craft-features”的影像組學方法，本論文對CT上特定IA亞型的預測進行了兩步驟的研究。首先，我們收集了“近純”的 IA 亞型數據，以盡量減少組織異質性的影響，達到提取特定 IA 亞型代表性的放射組學特徵之目的。此外，針對IA亞型間多樣的CT外觀，本研究提出了Component Difference Texture Features（CDTF）來描述每個CT灰階強度區域的特徵，並開發了Competing One-Vs-One（COVO）分類器，以達到五類IA亞型的多類別分類。透過“近純”的數據和提出的方法，所提出的模型對五個 IA 亞型和三個預後等級的預測分別達到了 86% 和 92% 的準確率。此外，對於五種 IA 亞型的分類，所提出基於 CDTF 特徵的 COVO 模型可以優於常規和先前研究改進的 OVO 方法（P < 0.05），並且明顯優於僅基於傳統放射組學特徵的 COVO模型（P < 0.05）。這些結果表明，“近純”數據有潛力能提取出具IA亞型鑑別力的放射組學特徵。此外，從每個CT灰階強度區域中提取的特徵可以提供比僅從整個病變區域提取的特徵提供更多的亞型預測信息，並且 COVO 有望減少多類分類的無能力問題的影響，提高五個 IA 亞型分類的性能。 第二步驟中，由於high-grade亞型(Micropapillary 和 Solid)在生存率和復發風險上的預後價值，我們專注於基於CT的high-grade亞型檢測。利用提取自“近純”數據的放射組學特徵，本研究建立了一個patch-wise預測模型，利用局部信息預測腫瘤區域內每個體素的high-grade亞型，進一步減少腫瘤內異質性的影響。 基於 patch-wise 模型，該模型實現了 0.85 的 AUC。 此外，考慮到high-grade亞型與 Spread Through Air Spaces (STAS) 之間的高度關聯，我們使用high-grade亞型的“近純”影像組學特徵來預測肺腺癌中的 STAS，並獲得了 0.83 的 AUC。 這些結果表明，patch-wise 模型有可能用於high-grade亞型成分檢測，並且使用來自high-grade亞型的放射組學信息具有預測 STAS 的潛力。 對於基於 DL 的放射組學方法，為了克服醫學數據樣本量有限的困難，我們提出了具有較少層數的Solid Attenuation Components Attention Deep Learning (SACA-DL) 模型，以防止模型過度擬合和訓練不足。 同時，考慮到侵入性組織學模式和Solid Attenuation Components（SACs）之間的關聯，SACA-DL 被建構為引導模型的特徵提取專注於 SACs 區域。 SACA-DL模型可以達到0.91的AUC，用於預測high-grade亞型的存在，並且顯著優於所提出的patch-wise模型、沒有專注 SACs 區域的DL模型和Consolidation/Tumor ratio (C/T ratio)（P<0.05）。 這些結果表明，使用來自 SACs 的信息可以潛在地提高預測高級成分的性能，並且 SACA-DL 有可能為高級成分提供比C/T ratio和放射組學特徵更多的預測信息。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:35:24Z (GMT). No. of bitstreams: 1 U0001-2210202115562900.pdf: 8097382 bytes, checksum: 7efa3e43fc927b453aabbc09f9bb9da0 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 i 誌謝 ii 中文摘要 iii ABSTRACT v CONTENTS viii LIST OF FIGURES xii LIST OF TABLES xviii Chapter 1 Introduction 1 1.1. Clinical Background 1 1.2. Background of Basic Technologies 4 1.3. Problem Description 7 1.3.1. The difficulty of intratumoral histological heterogeneity 8 1.3.2. The difficulty of varying CT appearance among IA subtypes 9 1.3.3. The difficulty of limited medical imaging sample size for DL model training 11 1.4. Related Works 11 1.4.1. Qualitative Features Analysis for ADCs Subtyping 11 1.4.2. Quantitative Features Analysis for ADCs Subtyping 12 1.4.3. Radiomics Analysis for IA subtyping 13 1.5. Overview of the Research Framework 17 Chapter 2 “Near-pure” Invasive Adenocarcinoma Subtypes Classification on Pulmonary CT 20 2.1. Introduction 20 2.2. Materials and Methods 21 2.2.1. Participants and Image Acquisition 21 2.2.2. Image Analysis 23 2.2.3. Previous OVO Methods for Non-competent Problem 35 2.2.4. Statistical Analysis 36 2.3. Results 36 2.3.1. Patients’ Subtypes Information 36 2.3.2. Performance of COVO for IA Subtyping and Prognostic Grading 37 2.3.3. Comparing Conventional Radiomics Features with Combination Features 38 2.3.4. Comparing Other OVO Methods with COVO 39 2.4. Discussion 43 Chapter 3 Prediction of Micropapillary and Solid Pattern and Spread Through Air Spaces (STAS) in Lung Adenocarcinoma on Pulmonary CT 46 3.1. Introduction 46 3.1.1. Prediction of Micropapillary and Solid Pattern 46 3.1.2. Prediction of Spread Through Air Spaces 47 3.2. Materials and Methods 49 3.2.1. Participants and Image Acquisition 49 3.2.2. Image Analysis 51 3.2.3. Statistical Analysis 57 3.3. Results 58 3.3.1. Demographic and Radiomic Features of High-grade Subtypes 58 3.3.2. Demographic and Radiomic Features of STAS 60 3.3.3. Patch-wise Prediction Model Applied to Cohort 1 and 2 for High-grade Subtypes 63 3.3.4. Patch-wise Prediction Model Applied to Cohort 3 and 4 for STAS 67 3.3.5. Comparison of the Prediction Results between the Patch-wise and Whole-lesion-based Predictions for High-grade Subtypes 70 3.3.6. Comparison of the Prediction Results between the Patch-wise Model and Radiological Invasiveness for High-grade Subtypes 72 3.3.7. Combined Models Applied to Cohort 1 and 2 for High-grade Subtypes 72 3.3.8. Comparison of the prediction results between current study and previously published studies for STAS 73 3.4. Discussion 74 3.4.1. Prediction of Micropapillary and Solid Pattern 74 3.4.2. Prediction of Spread Through Air Spaces 77 Chapter 4 Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Subtypes in Lung Adenocarcinoma 80 4.1. Introduction 80 4.2. Materials and Methods 81 4.2.1. Participants and Image Acquisition 81 4.2.2. Nodule Annotation and Data Preprocessing 83 4.2.3. Solid Attenuation Components Attention Deep Learning Model (SACA-DL) Development 84 4.2.4. Statistical Analysis 86 4.3. Results 87 4.3.1. Demographics 87 4.3.2. Performance of SACA-DL 89 4.3.3. Comparing the DL Model Without SACs Attention Channel with SACs Attention Deep Learning Model 97 4.3.4. Comparing the Prior Radiomics-Based Model with SACs Attention Deep Learning Model 97 4.3.5. Comparing the C/T ratio with SACs Attention Deep Learning Model 98 4.4. Discussion 99 Chapter 5 Conclusions 102 5.1. Summary and Contributions 102 5.2. Future Works 104 Reference 106 Appendix 123 A.1. CT Acquisition in National Taiwan University Hospital Hsin-Chu Branch 123 A.2. CT Acquisition in National Taiwan University Hospital 125 A.3. CT Acquisition in University of Texas MD Anderson Cancer Center 128 A.4. Disease-free-survival and Overall Survival Curve for STAS Prediction in each Tumor Stage 130 | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 電腦斷層 | zh_TW |
| dc.subject | 放射體學 | zh_TW |
| dc.subject | 組織學亞型 | zh_TW |
| dc.subject | 肺腺癌 | zh_TW |
| dc.subject | 腫瘤內組織學異質性 | zh_TW |
| dc.subject | Intratumoral histological heterogeneity | en |
| dc.subject | Computed tomography | en |
| dc.subject | Deep learning | en |
| dc.subject | Radiomics | en |
| dc.subject | Histological subtypes | en |
| dc.subject | Lung adenocarcinoma | en |
| dc.title | 電腦斷層掃描之侵犯性肺腺癌組織學亞型的術前預測 | zh_TW |
| dc.title | Preoperative Prediction of Invasive Adenocarcinoma Histological Subtypes in Computed Tomography | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0002-3618-0256 | |
| dc.contributor.advisor-orcid | 陳中明(0000-0002-0023-5817) | |
| dc.contributor.oralexamcommittee | 潘亭壽(Hsin-Tsai Liu),張允中(Chih-Yang Tseng),林孟暐,林文澧 | |
| dc.subject.keyword | 肺腺癌,組織學亞型,放射體學,深度學習,腫瘤內組織學異質性,電腦斷層, | zh_TW |
| dc.subject.keyword | Lung adenocarcinoma,Histological subtypes,Radiomics,Deep learning,Intratumoral histological heterogeneity,Computed tomography, | en |
| dc.relation.page | 138 | |
| dc.identifier.doi | 10.6342/NTU202104034 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-11-01 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-11-01 | - |
| 顯示於系所單位： | 醫學工程學研究所 | |
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