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
相關次數：	點閱：2 下載：0
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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 



                                

Abdel Razek AA, Kandeel AY, Soliman N, et al. Role of diffusion-weighted echo-planar MR imaging in differentiation of residual or recurrent head and neck tumors and posttreatment changes. AJNR Am J Neuroradiol 2007;28:1146–1152.
Abdel Razek AA, Soliman NY, Elkhamary S, et al. Role of diffusion-weighted MR imaging in cervical lymphadenopathy. Eur Radiol 2006;16:1468-1477
Ahmad A, Branstetter BFT. CT versus MR: still a tough decision. Otolaryngol Clin North Am 2008;41:1–22.
Bezdek JC, Ehrlich R, and Full W, “FCM: The fuzzy c-means clustering algorithm,” Computers and Geosciences 1984; 10: 191-203
Chen W, Giger ML, Li H, Bick U, and Newstead GM, “Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images,” Magnetic Resonance in Medicine 2007; 58: 562-571
Chikui T, Yonetsu K, Nakamura T. Multivariate feature analysis of sonographic findings of metastatic cervical lymph nodes: contribution of blood flow features revealed by power Doppler sonography for predicting metastasis. AJNR Am J Neuroradiol 2000;21:561–567
Chuang KS, Tzeng HL, Chen S, Wu J, and Chen TJ, “Fuzzy c-means clustering with spatial information for image segmentation,” Computerized Medical Imaging and Graphics 2006; 30: 9-15
Crippa F, Leutner M, Belli F, et al. Which kinds of lymph node metastases can FDG-PET detect? a clinical study in melanoma. J Nucl Med 2004;41:1491–1494
Curtin HD, Ishwaran H, Mancuso A, Dalley RW, Caudry DJ, McNeil BJ. Comparison of CT and MR imaging in staging of neck metastases. Radiology 1998;207:123–130.
de Bondt RB et al., “Diagnostic accuracy and additional value of diffusion-weighted imaging for discrimination of malignant cervical lymph nodes in head and neck squamous cell carcinoma,” Neuroradiology 2009; 51: 183-192
DeLong ER, DeLong DM, and Clarke-Pearson DL, “Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach,” Biometrics 1998; 837-845
Deoni SC, Rutt BK, Parrent AG, and Peters TM, “Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T,” Neuroimage 2007; 34: 117-126
Dunn JC “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” Journal of Cybernetics 1973; 3: 32-57
Edge SE, Byrd DR, Compton CC, et al., AJCC Cancer Staging Manual, Springer, New York, NY, USA, 7th edition, 2009
Eida S, Sumi M, Sakihama N, Takahashi H, and Nakamura T, “Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy,” American Journal of Neuroradiology 2007; 28: 116-121
Gray L, MacFall J. Overview of diffusion imaging. Magn Reson Imaging Clin N Am 1998;6:125-138
Herneth AM, Czerny C, Krestan C, et al. Role of Diffusion Weighted MRI in the Characterization of Lymph node metastases. XVI International Congress of Head and Neck Radiology. Frankfurt/Main, Germany; 4-6 Septemper 2003:C12
Hudgins PA, Anzai Y, Morris MR, et al. Ferumoxtran-10, a superparamagnetic iron oxide as a magnetic resonance enhancement agent for imaging lymph nodes: a phase 2 dose study. AJNR Am J Neuroradiol 2002;23:649-656
Imaizumi A, Yoshino N, Yamada I, et al. A potential pitfall of MR imaging for assessing mandibular invasion of squamous cell carcinoma in the oral cavity. AJNR Am J Neuroradiol 2006;27:114–122.
Johnson JT. A surgeon looks at cervical lymph nodes. Radiology 1990;175:607-610
Kim S, Loevner L, Quon H, et al. Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 2009;15:986–994.
King AD, Ahuja AT, Yeung DK, et al. Malignant cervical lymphadenopathy: diagnostic accuracy of diffusion-weighted MR imaging. Radiology 2007;245:806-813
Lee MC, Tsai HY, Chuang KS, Liu CK, and Chen MK, “Prediction of nodal metastasis in head and neck cancer using a 3T MRI ADC map,” American Journal of Neuroradiology 2013; 34: 864-869
Lopes R et al., “Prostate cancer characterization on MR images using fractal features,” Medical physics 2011; 38: 83-95
Maeda M, Kato H, Sakuma H, Maier SE, Takeda K. Usefulness of the apparent diffusion coefficient in line scan diffusion-weighted imaging for distinguishing between squamous cell carcinomas and malignant lymphomas of the head and neck. AJNR Am J Neuroradiol 2005;26:1186–1192.
Maes F, Collignon A, Vandermeulen D, Marchal G, and Suetens P, “Multimodality image registration by maximization of mutual information,” Medical Imaging, IEEE Transactions on 1997; 16: 187-198
Markkola AT, Aronen HJ, Paavonen T, et al. Spin lock and magnetization transfer imaging of head and neck tumors. Radiology 1996;200:369-375
Mayerhoefer ME, Breitenseher M, Amann G, and Dominkus M, “Are signal intensity and homogeneity useful parameters for distinguishing between benign and malignant soft tissue masses on MR images?: Objective evaluation by means of texture analysis,” Magnetic Resonance Imaging 2008; 26: 1316-1322
Mukherji SK, Schiro S, Castillo M, Kwock L, Muller KE, Blackstock W. Proton MR spectroscopy of squamous cell carcinoma of the extracranial head and neck: in vitro and in vivo studies. AJNR Am J Neuroradiol 1997;18:1057–1072.
Neil JJ. Diffusion imaging concepts for clinicians. J Magn Reson Imaging 2008;27(1):1–7.
Ng SH, Yen TC, Liao CT et al. 18F-FDG PET and CT/MRI in Oral Cavity Squmous Cell Carcinoma: A Prospective Study of 124 Patients with Histologic Correlation. J Nucl Med 2005;46:1136-1143
Padhani AR, Liu G, Mu-Koh D, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11:102–125.
Padhani AR, Makris A, Gall P et al., “Therapy Monitoring of Skeletal Metastases With Whole-Body Diffusion MRI. Journal of Magnetic Resonance Imaging 2014; 39:1049-1078
Perrone A, Guerrisi P, Izzo L, et al. Diffusion-weighted MRI in cervical lymph nodes: Differentiation between benign and malignant lesions. European Journal of Radiology 2011;77:281-286
Razek AA, Megahed AS, Denewer A, et al. Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors. Acta Radiol 2008;49:364–370.
Rose CJ et al., “Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps,” Magnetic Resonance in Medicine 2009; 62: 488-499
Sanjeev Chawla, Sungheon Kim, Sumei Wang et al. Diffusion-weighted imaging in head and neck cancers. Future Oncol. 2009 September ; 5(7): 959–975.
Som PM. Detection of metastasis in cervical lymph nodes: CT and MR criteria and
Srinivasan A et al., “Utility of the k-means clustering algorithm in differentiating apparent diffusion coefficient values of benign and malignant neck pathologies,” American Journal of Neuroradiology 2010; 31: 736-740
Srinivasan A, Dvorak R, Perni K, et al. Differentiation of benign and malignant pathology in the head and neck using 3T apparent diffusion coefficient values: early experience. AJNR Am J Neuroradiol 2008;29:40-44
Star-Lack JM, Adalsteinsson E, Adam MF, et al. In vivo1h MR spectroscopy of human head and neck lymph node metastasis and comparison with oxygen tension measurements. AJNR Am J Neuroradiol 2000;21:183–193.
Stuckensen T, Kovacs AF, Adams S, Baum RP. Staging of the neck in patients with oral cavity squamous cell carcinomas: a prospective comparison of PET, ultrasound, CT and MRI. J Craniomaxillofac Surg 2000;28:319 –324
Sumi M, Ohki M, Nakamura T. Comparison of sonography and CT for differentiating benign from malignant cervical lymph nodes in patients with head and neck squamous cell carcinomas. AJR Am J Roentgenol 2001;176:1019–1024
Sumi M, Sakihama N, Sumi T, et al. Discrimination of Metastatic Cervical Lymph Nodes with Diffusion-Weighted MR Imaging in Patients with Head and Neck Cancer. AJNR Am J Neuroradiol 2003;24:1627-1634
Sumi M, Takagi Y, Uetani M, et al. Diffusion-weighted echoplanar MR imaging of the salivary glands. AJR Am J Roentgenol 2002;178:959-965
Sun H, Wang S, and Jiang Q, “FCM-based model selection algorithms for determining the number of clusters,” Pattern recognition 2004; 37: 2027-2037
Thoeny HC and Keyzer F. De, “Extracranial applications of diffusion-weighted magnetic resonance imaging,” European Radiology 2007; 17: 1385-1393
Thoeny HC, Ross BD. Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 2010;32:2-16
van den Brekel MW, Castelijns JA, Snow GB. Detection of lymph node metastases in the neck: radiologic criteria. Radiology 1994;192:617-618
van den Brekel MW, Castelijns JA, Snow GB. The size of lymph nodes in the neck on sonograms as a radiologic criterion for metastasis: how reliable is it? AJNR Am J Neuroradiol 1998;19:695-700
van den Brekel MWM, Stel HV, Castelijns JA, et al. Cervical lymph node metastasis:assessment of radiologic criteria. Radiology 1990; 177:379-384.
Vandecaveye V, De Keyzer F, Nuyts S, et al. Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: correlation between radiologic and histopathologic findings. Int J Radiat Oncol Biol Phys 2007;67:960–971.
Vandecaveye V, De Keyzer F, Vander Poorten V, et al. Head and neck squamous cell carcinoma: value of diffusion-weighted MR imaging for nodal staging. Radiology 2009;251:134-146
Wang J, Takashima S, Takayama F, et al. Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 2001;220:621-630
Yamazaki Y, Saitoh M, Notani KI et al. Assessment of cervical lymph node metastases using FDG-PET in patients with head and neck cancer. Ann Nucl Med 2008;22:177-184
Yoshino N, Yamada I, Ohbayashi N, et al. Salivary glands and lesions: evaluation of apparent diffusion coefficients with split-echo diffusion-weighted MR imaging--initial results. Radiology 2001;221:837-842
Yousem DM, Som PM, Hackney DB, et al. Central nodal necrosis and extracapsular neoplastic spread in cervical lymph nodes: MR imaging versus CT. Radiology 1992;182:753-759
Yousem DM. Dashed hopes for MR imaging of the head and neck: the power of the needle. Radiology 1992;184:25–26.
Zhang Y et al., “Apparent diffusion coefficient values of necrotic and solid portion of lymph nodes: differential diagnostic value in cervical lymphadenopathy,” Clinical Radiology 2013; 68: 224-231

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