臺灣博碩士論文加值系統

Fuhrman核分級可以做為腫瘤患者的標準治療資訊。探討合併電腦斷層掃描紋理分析與機器學習電腦斷層掃描圖像區分低和高Fuhrman核等級腎細胞癌的準確性。回顧性病例對照設計中，腎細胞癌患者選自癌症影像圖譜資料庫，使用機器學習分析電腦斷層掃描灰階矩陣特徵預測低和高Fuhrman核等級腎細胞癌。由專家手動圈選電腦斷層掃描橫截面圖像。對手動圈選病變進行紋理分析，並使用交叉驗證檢查重現性。評估了關於灰階分布圖(histogram)、 形態特徵、灰階共生(co-occurrence) 矩陣的紋理特徵和紋理特徵 (Gabor)，選擇最具有區別性的特徵來成功分類低和高Fuhrman核等級腎細胞癌。通過驗證(validation)來評估電腦斷層掃描紋理特徵的診斷準確性。向量支持分類器放入70個特徵比較來區分多偵測器電腦斷層掃描圖像上的兩種類型的腎細胞癌顯示接受者操作特徵曲線下面積的統計有最佳的面積(0.82)。產生的電腦輔助診斷系統可以提供泌尿科醫生腫瘤分級的建議，作為醫療決策輔助。

Fuhrman nuclear grading can add a piece of information to the standard care of patient with clear cell renal cell carcinoma. To explore the accuracy of texture analysis to distinguish between high-grade and low-grade Fuhrman nuclear grades of clear cell renal cell carcinoma on computed tomography images. In a retrospective case-control design, patients with RCC are selected from the “The Cancer Image Atlas” (TCIA) database. Cross-sectional computerized tomography (CT) images were contoured manually by experts. Texture analysis was done for each lesion, and reproducibility was examined by validation. Image features regarding morphology feature, the gray-level histogram, gray-level co-occurrence matrix and Gabor texture feature, were assessed. The most relevant features were chosen to generate classifiers. Diagnostic accuracy of texture features was evaluated by validation. Support Vector Machine (SVM) classifiers using 70 features demonstrated optimal area under receiver operating characteristic (AUROC) curve (0.82) statistics. When the morphology features, intensity features and texture features are combined in classifiers, a computer-aided diagnosis (CAD) system is generated. The developed CAD system may give suggestions of tumor grading to the urologists as an aid in decision making.

Table of Contents

Chapter I Introduction 1
1.1. BACKGROUND: 1
Chapter II Literature review 9
2.1 CONVENTIONAL IMAGE FINDINGS OF DIFFERENT TYPES OF RCC 9
2.2 TREATMENT OF RCC 9
2.3 PROGNOSIS OF RCC 10
2.4 ERA OF COMPUTER-AIDED DIAGNOSTIC SYSTEMS AND CT TEXTURE ANALYSIS OF MALIGNANCY 12
Chapter III Materials and Methods 14
3.1 PATIENT SELECTION 15
3.2 STANDARD CT TECHNIQUE 15
3.3 IMAGE PROCESSING 15
3.4 Tumor contour delineation 16
3.5 CT TEXTURE ANALYSIS 17
3.6 EXTRACTION OF QUANTITATIVE FEATURE 18
3.7 FEATURE SELECTION 31
3.8 ELEVEN TYPES OF CLASSIFIERS USED IN THIS STUDY(WITTEN, FRANK, HALL, & PAL, 2016) 31
3.9 VALIDATION METHODS OF MACHINE LEARNING(BOUCKAERT ET AL., 2016) 35
3.10 EVALUATION CRITERIA OF CLASSIFIERS(XU, GU, WANG, WANG, & QIN, 2019) 35
3.11 STATISTICAL ANALYSES 36
Chapter IV Results 38
4.1 MANUAL CONTOURING OF TUMORAL ROI 38
4.2 RESULTS OF EXTRACTION OF IMAGE FEATURES AND FEATURE SELECTION 38
4.3 PERFORMANCE OF CLASSIFIERS RESULTS VALUE FOR PREDICTION OF HIGH AND LOW NUCLEAR GROUPS 42
Chapter V Discussion 45
5.1 INTERPRETATION OF FINDINGS 45
5.2 LIMITATION OF THIS STUDY 46
Chapter VI Conclusion and Suggestion 47
Chapter VII English References 48
Chapter VIII Appendix 60

List of Tables
TABLE 1 Familial RCC 3
Table 2 Fuhrman’s grading system 7
TABLE 3 Comparison of AJCC TMN 7th edition and 8th edition of staging of RCC 11
TABLE 4 Summary of classifiers results value with feature selection methods 40
TABLE 5 Performance of classifiers results value by attributeselectedclassifier with selected features space for prediction of high and low nuclear groups 43

List of Figures
Figure 1 Biological functions of the von Hippel- Lindau protein 5
Figure 2. Mammalian target of rapamycin integration with hypoxia-inducible factors. 6
Figure 3 Patients with contrast-enhanced CT images of three common subtypes of RCC 8
Figure 4. Flow chart of medical image analysis 15
Figure 5. The circular black blocks depict perimeter, the sum of white blocks represent area 18
Figure 6. The smallest rectangle containing the contoured tumor is used as an example for morphology feature 19
Figure 7. Mean and standard deviation of gray level histogram. 20
Figure 8. Distribution curves with different variance 21
Figure 9. Kurtosis and skewness. 21
Figure 10. Original image matrix used to create GLCM. 29
Figure 11. Creation of grays level co-occurrence matrix from image matrix 29
Figure 12. Tumor contour delineation of tumor region of interest 38

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