本研究調查使用心臟磁共振（CMR）動態影像的放射組學分析來診斷和預測法布瑞氏症（FD）患者的左心室壁厚（LVWT）變化。我們分析了23名FD患者和18名正常志願者的數據，利用放射組學來區分FD病例與正常對照，以及區分有無左心室肥厚（LVH）的FD亞組。研究包括四個任務：區分FD和正常病例、區分無LVH的FD和正常病例、區分LVH和無LVH的FD，以及預測LVWT穩定性。主要發現包括識別出對模型準確性至關重要的最佳放射組學特徵，如形狀球形度（Shape Sphericity）、灰階區域大小非均勻性（GLSZM SZNN）和灰階共生矩陣相關性（GLCM Correlation）。研究強調了早期檢測和持續監測心臟受累情況的重要性。我們的結果顯示，整合3D和2D特徵並使用ANOVA特徵選擇方法的模型表現最佳，優於僅使用CMR指標的模型。分析發現，Mid和Basal特徵相比於3D特徵更頻繁地被選中，這表明心肌纖維化的顯著作用，纖維化通常出現在單個切片中。此外，研究強調了及早開始酵素替代療法（ERT）和持續心臟監測以有效管理FD進展的必要性。這些發現強調了放射組學分析在改善FD患者臨床管理中的潛力。

This study investigates the use of radiomic analysis on cardiac magnetic resonance (CMR) cine images to diagnose and predict left ventricular wall thickness (LVWT) outcomes in Fabry disease (FD) patients. We analyzed data from 23 FD patients and 18 normal volunteers, employing radiomics to differentiate FD cases from normal controls and between FD subgroups with and without left ventricular hypertrophy (LVH). The study comprised four tasks: differentiating FD from normal cases, distinguishing non-LVH FD from normal cases, differentiating LVH FD from non-LVH FD, and predicting LVWT stability. Key findings include the identification of optimal radiomic features such as Shape Sphericity, GLSZM SZNN, and GLCM Correlation, which were crucial for model accuracy. The study highlighted the importance of early detection and ongoing monitoring of cardiac involvement in FD. Our results demonstrated that integrating 3D and 2D features with ANOVA feature selection yielded the best model performance, outperforming the CMR indices model. The analysis revealed that Middle and Base features were more frequently selected compared to 3D features, suggesting a significant role of myocardial fibrosis, which often appears in single slices. Furthermore, the study emphasized the need for early enzyme replacement therapy (ERT) initiation and continuous cardiac monitoring to manage FD progression effectively. These findings underscore the potential of radiomic analysis in improving the clinical management of FD patients.

摘要 i
Abstract ii
Contents iii
Figures vi
Tables xii
Chapter 1 Introduction 1
1.1 Fabry disease 1
1.2 Diagnosis and monitoring of FD in CMR 2
1.3 Radiomic Analysis 6
1.4 Motivations and Orientation 9
Chapter 2 Theory 10
2.1 Balanced Steady State Free Precession Imaging 10
2.2 Modified Look-Locker Inversion Recovery 10
Chapter 3 Methods 13
3.1 Study Cohort 13
3.1.1 Task 1: Normal vs. All FD 14
3.1.2 Task 2: Normal vs. non-LVH FD 14
3.1.3 Task 3: non-LVH FD vs. LVH FD 15
3.1.4 Task 4-1: max. LVWT stable vs. max. LVWT increase 15
3.1.5 Task 4-2: mean LVWT stable vs. mean LVWT increase 16
3.1.6 Task 4-3: LVMi stable vs. LVMi increase 16
3.2 MRI Acquisition 17
3.2.1 Balanced Steady State Free Precession Imaging 17
3.2.2 Modified Look-Locker inversion recovery 17
3.3 Image Preprocessing 18
3.4 Radiomic Analysis 19
3.4.1 Segmentation 19
3.4.2 Feature Extraction 20
3.4.3 Model Training 22
3.5 Measurement of LVWT 26
3.6 Measurement Native T1 Value 27
3.7 CMR indices 27
3.8 Intraobserver Variability of max. LVWT 28
3.9 Statistical Analysis 28
Chapter 4 Results 29
4.1 Demographics 29
4.2 Task 1: Normal vs. All FD 35
4.2.1 Bin widths and Number of Selected Features 35
4.2.2 Class Weight-adjusted vs. Without Weight-adjusted 37
4.2.3 Model Performance 38
4.2.4 Top 5 Selected Features 40
4.3 Task 2: Normal vs. non-LVH FD 48
4.3.1 Model Performance 48
4.3.2 Top 10 Selected Features 50
4.4 Task 3: non-LVH FD vs. LVH FD 58
4.4.1 Model Performance 58
4.4.2 Top 15 Selected Features 60
4.5 Task 4-1: max. LVWT stable vs. max. LVWT increase 70
4.5.1 Bin widths and Number of Selected Features 70
4.5.2 Model Performance 80
4.5.3 Top 20 Selected Features 84
4.6 Task 4-2: mean LVWT stable vs. mean LVWT increase 91
4.6.1 Bin widths and Number of Selected Features 91
4.6.2 Model Performance 101
4.6.3 Top 30 Selected Features 104
4.7 Task 4-3: LVMi stable vs. LVMi increase 110
4.7.1 Bin widths and Number of Selected Features 110
4.7.2 Model Performance 120
4.7.3 Top 25 Selected Features 123
Chapter 5 Discussions 129
5.1 Demographics of Study Cohorts 129
5.2 Radiomics and Classification Model Parameters 132
5.3 Task 1: Normal vs. All FD 133
5.4 Task 2: Normal vs. non-LVH FD 136
5.5 Task 3: non-LVH FD vs. LVH FD 139
5.6 Task 4-1: max. LVWT stable vs. max. LVWT increase 142
5.7 Task 4-2: mean LVWT stable vs. mean LVWT increase 147
5.8 Task 4-3: LVMi stable vs. LVMi increase 151
5.9 Summary of all Tasks 154
Chapter 6 Conclusions 157
6.1 Limitations 157
6.2 Conclusions 158
6.3 Future Works 159
Chapter 7 Appendix 160
7.1 Abbreviation List 160
7.2 Definition List of Quantitative Indices 163
7.3 Definitions of Selected Radiomic Features 164
7.4 Top 20 3D+2D features selected by ANOVA in Task 4-1 167
7.5 Top 30 3D+2D features selected by ANOVA in Task 4-2 170
7.6 Top 25 2D features selected by chi2 in Task 4-3 174
7.7 Gap Years of Study Cohort: Interval Times and LVWT Changes 176
7.8 Response of Oral Defense 179
7.9 Ratio of Radiomic Feature Values in End-Diastolic and End-Systolic Phases 182
7.9.1 Method 182
7.9.2 Task 1: Normal vs. All FD 182
7.9.3 Task 2: Normal vs. non-LVH FD 184
7.9.4 Task 3: non-LVH FD vs. LVH FD 186
7.9.5 Task 4-1: max. LVWT stable vs. max. LVWT increase 188
7.9.6 Task 4-2: mean LVWT stable vs. mean LVWT increase 190
7.9.7 Task 4-3: LVMi stable vs. LVMi increase 191
Chapter 8 References 193
Plagiarism Detection 200

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