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研究生： 黃品勳
Huang, Pin Hsun
論文名稱： 攝護腺DCE-MRI:利用分布圖分析區分移行區和周邊區之攝護腺腫瘤
Prostate DCE-MRI: Discriminate transitional zone and peripheral zone prostate cancer by using histogram analysis
指導教授： 王福年
Wang, Fu Nien
口試委員: 劉益瑞
Liu, Yi Jui
彭旭霞
Peng, Hsu Hsia
學位類別： 碩士
Master
系所名稱： 原子科學院 - 生醫工程與環境科學系
Department of Biomedical Engineering and Environmental Sciences
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 43
中文關鍵詞： 動態對比劑顯影 、攝護線腫瘤 、良性攝護腺增生 、腫瘤內異質性 、分布圖分析
外文關鍵詞： DCE-MRI, prostate cancer, benign prostatic hyperplasia, tumor vascular heterogeneity, histogram analysis
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    • 動態對比劑顯影核磁共振影像應用於腫瘤偵測上的主要原理為利用腫瘤血管新生的特性，經由注射對比劑改變組織訊號值，觀察時間訊號曲線，利用定量或半定量方式得到相關參數用來評估攝護腺腫瘤，實際臨床上，攝護腺移行區會發生良性攝護腺增生，是一種富血管性的良性變化，目前診斷技術上最大的困難即為分辨攝護腺腫瘤和良性攝護腺增生，本篇研究希望藉由腫瘤內異質性來區分兩者。
    近年，腫瘤內異質性被大家廣泛研究，分布圖分析和紋理分析皆有研究用 來觀察腫瘤異質性，本篇研究使用分布圖分析觀察半定量參數 (流入斜率、延遲期斜率) 分布，比較腫瘤組織和正常組織間的分布參數，將分布參數應用於區分攝護腺腫瘤和良性攝護腺增生。

    The principle of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) applying in tumor detection is based on tumor angiogenesis. DCE-MRI can noninvasively assess tissue vascularity by injecting contrast agent and time intensity curve can be obtained then analyzed by quantitative or semi-quantitative approach.
    Benign prostatic hyperplasia (BPH) is a benign condition with hyper-vascularity within the TZ. This benign condition makes that detecting prostate in the transitional zone (TZ) is still a challenge.
    The hypothesis in this study is that tumor vascular heterogeneity could help to discriminate the TZ prostate cancer from BPH. In this study, histogram analysis in wash in slope map and delay phase slope map were carried out on the DCE-MRI data of the patients diagnosed with prostate cancer. Several indexes were evaluated to investigate the efficiency on differentiating prostate cancer from BPH.

    Contents 摘要 i 致謝 ii Abstract iii Contents iv Chapter 1 Introduction 1 Chapter 2 Theory 4 2.1 Prostate anatomy & pathology 4 2.2 Diagnosis of prostate cancer 5 2.3 Dynamic contrast enhanced MRI 6 Chapter 3 Materials and methods 8 3.1 Patients 8 3.2 MRI 8 3.3 Image Analysis 10 3.3.1 Parametric image 10 3.3.2 Histogram analysis 16 3.3.3 ROI selection 16 3.4 Statistical Analysis 19 Chapter 4 Results 20 4.1 Results from four algorithms 20 4.2 Histogram parameters of normal tissue and cancer 27 4.2.1 Histogram analysis in TZ 27 4.2.2 Histogram analysis in PZ 29 4.3 ROC analysis 31 Chapter 5 Discussion and conclusion 33 Appendix 42 References 43

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