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研究生:鄭暐達
研究生(外文):Cheng, Woeidar
論文名稱:以高解析血流都卜勒超音波影像進行乳癌前置化療成效預測
論文名稱(外文):Tumor Response Prediction To Neo-adjuvant Chemotherapy For Breast Cancer: Dependent On Ultrasonic Vascularity Morphology
指導教授:黃育仁黃育仁引用關係
指導教授(外文):Huang, Yulen
口試委員:蔡孟勳范耀中
口試委員(外文):Tsai, MenghsiunFan, Yaochung
口試日期:2012/06/20
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:43
中文關鍵詞:乳房腫瘤血管新生前置性化療三維高解析血流都卜勒超音波決策樹
外文關鍵詞:breast tumorangiogenesisneo-adjuvant chemotherapy3D High-definition flow Doppler ultrasounddecision tree
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血管新生是惡性腫瘤成長的一個重要因素,可以此特徵來評估術前輔助化療的效果,而腫瘤血管檢查工具多元,包含X光攝影、核磁共振造影及超音波等,其中X光具有高放射劑量的隱憂,而核磁共振造影則成本較高。採用超音波取得乳癌腫瘤血管雖然在影響上雜訊較高,但不具有放射性,並且成本較低。在本研究使用的高解析血流(high-definition flow, HDF)都卜勒超音波為都卜勒超音波的一項技術,除了能有效地發現腫瘤位置之外,並能提供血流方向以及強度資訊,對於乳癌腫瘤血管分析具有強大效益。
我們從乳癌腫瘤部位自動提取血管中心線後,擷取了11項關於血管的特徵,經由這11項特徵中篩選出8項較具指標特徵,將其中4項血管型態特徵、4項血流量化特徵以及合併後的8項特徵,分別進行分析研究。本論文採用了32筆具有化療第0至第3階段的病患資料,並將每階段之間的差異資料以決策樹歸納出不同化療階段變化中,評估預測化療反應的精準度,並以K-fold交叉驗證的方法實施交叉驗證,最後利用混淆矩陣來評估本論文提出的方法。
Tumor vascularity, an important factor correlated with tumor malignancy, can be used to evaluate the effect of the neo-adjuvant chemotherapy prior to surgery. High-definition flow (HDF) Doppler ultrasound is performed to investigate blood flow and solid directional flow information in breast tumors. In this study, vascularity morphology features from HDF power Doppler ultrasound imaging are extracted as early predictors for evaluate chemotherapy effects. Firstly, this study design an automatic method to extract vascular centre-lines from the tumor area. Then the vascularization indices are estimated from the extracted vascular centre-lines. Finally, a decision tree model with all characteristics is employed as a tumor response predictor to neo-adjuvant chemotherapy for breast cancer.
摘要.................................................................................................iii
ABSTRACT....................................................................................iv
TABLE OF CONTENTS...............................................................v
LIST OF FIGURES........................................................................vi
CHAPTER 1....................................................................................1
INTRODUCTION.........................................................................1
CHAPTER 2....................................................................................5
MATERIALS...................................................................................5
2.1 Data Collection....................................................................5
2.2 Feature Extraction..............................................................9
CHAPTER 3.................................................................................15
METHODS..................................................................................15
3.1 Decision Tree.....................................................................15
3.2 Coding.................................................................................15
3.3 Neo-adjuvant Chemotherapy Response................17
3.4 Decision Tree Construction: Weka Software..........19
3.5 K-fold Cross-validation..................................................23
3.6 Performance Measures..................................................25
CHAPTER 4.................................................................................27
RESULTS.......................................................................................27
CHAPTER 5..................................................................................31
DISCUSSION AND CONCLUSION......................................31
REFERENCES...............................................................................33
[1]ACS, "Breast Cancer Facts and Figures 2011-2012," American Cancer Society, 2012.
[2]I. E. Smith, "Neoadjuvant/presurgical treatments," Breast Cancer Res, vol. 10 Suppl 4, p. S24, 2008.
[3]J. Folkman, "What is the evidence that tumors are angiogenesis dependent?," J Natl Cancer Inst, vol. 82, pp. 4-6, Jan 3 1990.
[4]J. Folkman, "Tumor angiogenesis," Adv Cancer Res, vol. 43, pp. 175-203, 1985.
[5]M. Riccabona, T. R. Nelson, and D. H. Pretorius, "Three-dimensional ultrasound: accuracy of distance and volume measurements," Ultrasound Obstet Gynecol, vol. 7, pp. 429-34, Jun 1996.
[6]D. R. Chen, R. F. Chang, W. J. Wu, W. K. Moon, and W. L. Wu, "3-D breast ultrasound segmentation using active contour model," Ultrasound Med Biol, vol. 29, pp. 1017-26, Jul 2003.
[7]N. J. Raine-Fenning, B. K. Campbell, J. S. Clewes, N. R. Kendall, and I. R. Johnson, "The reliability of virtual organ computer-aided analysis (VOCAL) for the semiquantification of ovarian, endometrial and subendometrial perfusion," Ultrasound Obstet Gynecol, vol. 22, pp. 633-9, Dec 2003.
[8]N. J. Raine-Fenning, J. S. Clewes, N. R. Kendall, A. K. Bunkheila, B. K. Campbell, and I. R. Johnson, "The interobserver reliability and validity of volume calculation from three-dimensional ultrasound datasets in the in vitro setting," Ultrasound Obstet Gynecol, vol. 21, pp. 283-91, Mar 2003.
[9]R. C. Gonzalez and R. E. Woods, Digital image processing, 3rd ed. Upper Saddle River, NJ ; Harlow: Pearson/Prentice Hall, 2008.
[10]I. Y. Jarvela, P. Sladkevicius, S. Kelly, K. Ojha, G. Nargund, and S. Campbell, "Three-dimensional sonographic and power Doppler characterization of ovaries in late follicular phase," Ultrasound Obstet Gynecol, vol. 20, pp. 281-5, Sep 2002.
[11]W. H. Kuo, C. N. Chen, F. J. Hsieh, M. K. Shyu, L. Y. Chang, P. H. Lee, L. Y. Liu, C. H. Cheng, J. Wang, and K. J. Chang, "Vascularity change and tumor response to neoadjuvant chemotherapy for advanced breast cancer," Ultrasound Med Biol, vol. 34, pp. 857-66, Jun 2008.
[12]M. J. Byrne and A. K. Nowak, "Modified RECIST criteria for assessment of response in malignant pleural mesothelioma," Ann Oncol, vol. 15, pp. 257-60, Feb 2004.
[13]E. F. Mark Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten, "The WEKA Data Mining Software: An Update," SIGKDD Explorations, vol. 11, 2009.
[14]S. R. Garner, "WEKA: The Waikato Environment for Knowledge Analysis," New Zealand Computer Science Research Students Conference, 1995.
[15]I. H. Witten and E. Frank, Data mining : practical machine learning tools and techniques with Java implementations. San Francisco, Calif.: Morgan Kaufmann, 2000.
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