利用磁振造影擴散影像ADC值判斷頭頸癌淋巴結轉移之良惡性

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研究生：	李明哲 Lee, Ming Che
論文名稱：	利用磁振造影擴散影像ADC值判斷頭頸癌淋巴結轉移之良惡性 Using DWI ADC value to distinguish malignant nodal metastasis from benign in head and neck cancers
指導教授：	莊克士 Chuang, Keh Shih
口試委員:	朱鐵吉林信宏陳穆寬蔡惠予
學位類別：	博士 Doctor
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：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	70
中文關鍵詞：	淋巴結、頭頸癌、磁振造影、擴散影像
外文關鍵詞：	lymph node, head and neck cancer, MRI, DWI
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

頸部淋巴結轉移的檢測用於預後和治療頭頸部腫瘤是相當重要的。本研究的目的是評估磁振造影擴散影像ADC值在3T磁振造影儀上區分良性和惡性淋巴結的能力。並且，以FCM 技術進一步分析淋巴結內部成份，得出不同成分的對應ADC值，以精確區分淋巴結的良惡性。
從2009年7月至2010年6月，共有22例（21男1女，平均年齡為49.8±9.5歲;年齡範圍，28-66歲）經由組織活體切片證實罹患頭頸癌，並已排定手術治療的患者皆含入此項研究。所有患者磁振造影及擴散影像皆以一個12通道頭部線圈及一個4通道頸部線圈的組在3.0T磁振造影儀進行造影（Verio的西門子，德國）。所有在擴散影像的淋巴結皆經ADC圖像量出ADC值。透過放射科醫師圈出每一病灶淋巴結外型，以MATLAB軟體 FCM分群運算得到病灶淋巴結不同成份分成2群及3群的對應ADC值。組織學研究結果是淋巴結轉移的診斷的參考標準。
在第一階段評估ADC值中，從b 值 0 and 800 s/mm2產生的信號強度導出的ADC值，良性淋巴結ADC值平均為1.086±0.222×10-3 mm2/s，惡性淋巴結為0.705 ± 0.118 × 10-3 mm2/s（P <0.0001）。當以0.851×10-3 mm2/s的ADC值作為用於區分惡性及良性淋巴結的閾值時，準確度為91.0％，靈敏度為91.1％，特異性為91.3％。在進一步分析淋巴結成份的FCM分群技術中，低數值ADC群在分辨良惡性淋巴結，其敏感性高達95.7%，遠遠高過整個淋巴結一起量取的ADC值(78.3%)。而且，其特異性也高達90%。ROC曲線也顯示低數值ADC群對惡性淋巴結有良好的分辨能力。
ADC值是一個敏感具特異性的參數，可以幫助區分良性及惡性淋巴結。但當惡性淋巴結中的成份有壞死部分，以ADC閥值來當成分辨良惡性的指標，常會因ADC值因壞死升高而將惡性誤判為良性。透過FCM分群技術，依不同成份得出不同ADC值，有效改善此判斷偏差。

The detection of cervical nodal metastases is important for the prognosis and treatment of head and neck tumors. The purposes of present study were first to assess the ability of apparent diffusion coefficient (ADC) values at 3.0T to distinguish malignant from benign lymph nodes and second to analyze ADC from partitions through a fuzzy C-means (FCM) technique for distinguishing nodal metastasis in head and neck cancer.
From July 2009 to June 2010, 22 patients (21 males and 1 female; mean age, 49.8 ± 9.5 years; age range, 28–66 years) scheduled for surgical treatment of biopsy-proven head and neck cancer were prospectively and consecutively enrolled in this study. All patients were scanned on a 3.0T imaging unit (Verio; Siemens, Germany) using a 12-channel head coil combined with a 4-channel neck coil. All lymph nodes seen on DWI images were proceeded ADC calculation from ADC maps. All lymph nodes also were analyzed using in-house software developed using MATLAB. A radiologist manually contoured the lesions, and ADC values for each lesion were divided into 2 (low and high) and 3 (low, intermediate, and high) partitions by using the FCM clustering algorithm. Histologic findings were the reference standard for the diagnosis of lymph nodes metastasis.
The ADC values derived from the signal intensity averaged across images obtained with b values of 0 and 800 s/mm2 was 1.086 ± 0.222 × 10-3 mm2/s for benign lymph nodes and 0.705 ± 0.118 × 10-3 mm2/s for malignant lymph nodes (P < .0001). When an ADC value of 0.851 × 10-3 mm2/s was used as a threshold value for differentiating benign from malignant lymph nodes, the best results were obtained with an accuracy of 91.0%, sensitivity of 91.3%, and specificity of 91.1%. From FCM technique, the results showed that the low value ADC clusters were more sensitive (95.7%) in distinguishing malignant from benign lesions than the whole-lesion mean ADC values (78.3%), while retaining a high specificity (approximately 90%). Moreover, receiver operating characteristic curves demonstrated that the low value ADC clusters used as a predictor of malignancy for lymph nodes could achieve a higher area under the curve (0.949 and 0.944 for 2 and 3 partitions, respectively).
In conclusion, ADC value is a sensitive and specific parameter that can help to differentiate malignant from benign lymph nodes. But, the ADC cutoff value to distinguish malignant lymph nodes was that metastatic nodes with necrotic areas might have higher ADC values because of necrosis and might be misidentified as benign. the FCM clustering technique as a computed aid to prevent this bias.

Content
Abstract    Ⅰ
中文摘要    Ⅲ
Acknowledgement    Ⅳ
Abbreviations    Ⅴ
Content    Ⅵ
List of Tables    Ⅷ
List of figures    Ⅸ
Preface                                                                                  XI
Overview of the dissertation                                                              XIII
Chapter 1. Introduction    1
1.1 Introduction    1
1.2 Literature review                                                                      5
1.2.1 DWI technique applies in Head and Neck cancers                                        5                                   
1.2.2 DWI technique and pulse sequence                                                      7                    
1.2.3 Quantitative Analysis with Diffusion weighted Imaging–ADC Mapping                         8                                                  
1.3 Specific aim                                                                        11
Chapter 2. ADC values in the distinguish of malignant from benign cervical lymph nodes    14
2.1. Background    14 
2.2 Motivation    15 
2.3Materials and method    16 
2.3.1Patients and Methods    16 
2.3.1.1 Patients    16 
2.3.1.2 MRI technique    18 
2.3.2 Image Analysis    19 
2.3.3 Pathologic evaluation    20 
2.3.4 Statistical Analysis    21 
2.4 Results    22 
2.5 Discussion       33 
Chapter 3. FCM clustering technique for distinguishing malignant and benign lymph nodes    36 

3.1. Background    36 
3.2. Motivation    36 
3.3. Materials and method    38 
3.3.1 Patient selection    38
3.3.2 MRI technique    38 
3.3.3 Pathological evaluation                                                         39
3.3.4 Image analysis                                                                40
3.3.4.1 DWI ADC calculation                                                     40
3.3.4.2 FCM technique                                                          40
3.3.5 Statistical analysis                                                             41
3.4    Results                                                                        43
3.5    Discussion                                                                     52
Chapter 4. Conclusions and Future Works    55 
4.1 Conclusions    55 
4.2 Future Works    57 
List of publications                                                                      58
Bibliography    62
Appendix                                                                  69

 
List of Tables 
Table 2.1. Tumor location, clinical tumor stages, and nodal stages according to TSE MR Imaging, histopothology, and DWI imaging    24 
Table 2.2. 3.0T ADC values in malignant neck lymph nodes.    25 
Table 2.3. ADC value based on supracentimeter and subcentimeter lymph node size    26 
Table 2.4. Comparisons of ADC values acquired from several references which studies head and neck lesions    27 
Table 3-1. ADC in 1-, 2-, and 3-cluster models    56 
Table 3-2. Sensitivity, specificity, and AUC with 95% confidence intervals for diagnosing malignant lymph nodes for different lymph node sizes by using ADC1, ADC2-L, and ADC3-L    57 



 
List of figures
Figure 1.1. Figure representation of a Computed tomography (CT)    4 
Figure 1.2. Figure representation of a magnetic resonance (MR)    4 
Figure 1.3. Figure representation of a single photon emission CT (SPECT)    4 
Figure 1.4. Figure representation of a photon emission tomography (PET)    4 
Figure 1.5. Diffusion-weighted sequence     12 
Figure 1.6. Graph illustrates the logarithm of relative signal intensity (SI) (y-axis) versus b value (x-axis) for tumor and normal tissue.    13 
Figure 2.1. Box and whisker plot presenting the scatter plot of mean apparent diffusion coefficients (ADCs) in all lymph nodes, subcentimeter and supracentimeter lymph nodes.                              28 
Figure 2.2. ROC curves were created for all lymph nodes, subcentimeter, and supracentimeter lymph nodes.    29 
Figure 2.3. (A) The axial T2-weighted MR image shows infiltrative neoplasm in right pyriform apex of hypopharynx and enlarged lymph nodes in bilateral level III, showing heterogeneous signal intensity    30 
(B)The axial post-gadolinium fat-suppressed T1-weighted FSE image reveals mild peripheral enhancement in right pyriform apex tumor(curve arrow) and heterogeneous enhancement in bilateral level III lymph nodes.    31 
(C) The ADC value within the right and left level III lymph node.    32 
(D) Corresponding H&E stained histopathologic slide shows intranodal tumor cells metastasis    33 
Figure 2.4. (A)The axial T2-weighted MR image shows subcentimeter lymph node with high signal intensity at right level V    34 
(B)The axial post-gadolinium fat-suppressed T1-weighted image    35 
(C)The ADC value was measured in the right level V lymph node.    36 
Figure 2.5. (A) The axial T2-weighted MR image shows no necrotic change of right level I lymph node    37 
(B) The axial T1-weighted MR image for lymph node    38 
(C) The axial post-gadolinium fat-suppressed T1-weighted FSE image reveals homogenous enhancement of right level I lymph node    39 
(D) The ADC value within the right level I lymph node    40 
Figure 3.1. (a) Axial post-gadolinium T1-weighted FSE MR image showing heterogeneous signal intensity   (b)malignant lymphadenopathy on ADC maps revealed an inhomogeneous intensity with color mapping                                                                    58
(c) The corresponding H&E stained histopathologic slide    59 
Figure 3.2. Overlap of ADC and cluster maps assuming 1 (a), 2 (b), 3 (c) clusters and their corresponding histograms (e–f)    60 
Figure 3.3. Box-and-whisker plot presenting the ADC of benign and malignant lymph nodes in 1-cluster, 2-cluster, and 3-cluster models    61 
Figure 3.4. ROC curves obtained through FCM analysis assuming a cluster number of 1, 2, and 3    62 
Figure 3.5. ROC curves for ADC1, ADC2-L, and ADC3-L in (a) subcentimeter and (b) supracentimeter lymph nodes    63 



                                

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