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系統識別號 U0026-2308201716161800
論文名稱(中文) 以正子斷層與磁振造影偵測鼻咽癌之外側咽後淋巴轉移
論文名稱(英文) Characterizing Lateral Retropharyngeal Nodal Spread for Nasopharyngeal Carcinoma using PET/CT and MRI
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 105
學期 2
出版年 106
研究生(中文) 王裕文
研究生(英文) Yu-Wen Wang
學號 P88971181
學位類別 博士
語文別 英文
論文頁數 118頁
口試委員 指導教授-張志涵
共同指導教授-鄭國順
口試委員-姚維仁
口試委員-林康平
口試委員-鍾相彬
口試委員-孫永年
中文關鍵字 回復係數  磁振造影  咽後淋巴結  鼻咽癌  類神經網路  正子斷層  診斷  放射治療 
英文關鍵字 Recovery coefficient  Magnetic resonance imaging  Retropharyngeal lymph nodes  Nasopharyngeal carcinoma  Neural Network  Positron emission tomography–computed tomography  Diagnosis  Radiation therapy 
學科別分類
中文摘要 目標: 確認鼻咽癌有無外側咽後淋巴結轉移至為重要。本研究前期的目標是看係數回復法在 FDG正子斷層檢查中,能有效增加正確率的範圍為何。其次,我們探討利用類神經網路的參數分析,是否能夠使影像例如磁振造影加上正子斷層診斷鼻咽癌有無外側咽後淋巴結轉移更加正確。最後,我們利用類似暴力法來找出比單一磁振造影軸面短徑因子更佳診斷鼻咽癌外側咽後淋巴結的多步驟評估法,因此,我們提出多步驟結合磁振造影與正子斷層參數新的診斷標準。
材料與方法: 第一群:本研究前期回溯性收集71鼻咽癌病人共142外側咽後淋巴結。磁振造影陽性之診斷標準為:中央壞死、包膜吃穿、不對稱性的非打非小的群聚淋巴結與經過放療磁振造影追蹤檢查下判斷為陽性者為之。正子斷層之陽性之診斷標準訂為 最大SUV值  2.5,正子影像中,和原發位置的鼻咽癌分不清楚的淋巴結均被排除。球體-背景比率法為文獻建立好的係數回復法,我們將它用於鼻咽癌外側咽後淋巴結的部份體積效應校正的工具,淋巴結候續依照三種大小範圍分類:1. < 6 毫米;2. 6至6.9毫米; 3. > 7毫米。第二群:來自中國的148病人共269外側咽後淋巴結。符合經過放療磁振造影追蹤檢查下判斷為陽性之診斷標準者為陽性。回溯性收集總共兩群病人計有219病人,共411外側咽後淋巴結,都進入上類神經網路和類似暴力法分析。類神經網路的4個參數分析包括磁振造影的軸面短徑、軸面長徑、冠面長徑與淋巴結平均SUV。4個參數的15 種組合都求出類神經網路正確率的結果,此結果和人類專家的以第一群142淋巴結做判讀比較。其次,每一參數的最佳切值也一併算出,我們用類似暴力演算法求出多步驟評估法最佳診斷組合,再以自助抽樣法(bootstrap sampling)評估新舊標準結果有無不同。
結果:係數回復法可以在正子影像中辨識的共88外側咽後淋巴結。其中,35淋巴結為陽性,53淋巴結為陰性。大小範圍在6至7毫米區間的,係數回復法有效提升 敏感度(20% 到 100%)與正確率(14% 到 71%)。大於7毫的,正確率亦可以從92%提昇到96%。後續研究中,類神經網路和類似暴力法分析,我們發現單一軸面短徑的正確率為89.1% (366/411最佳切值6.0毫米)。在4參數之類神經網路正確率結果,分別為89.0.9%,人為判斷的結果則為81.0% (115/142)。就預測準確度而論,相對單一軸面短徑, 4參數之多步驟評估法為372/411, 90.5%,增加6淋巴結之正確判斷。
結論:鼻咽癌外側咽後淋巴結只要是軸面短徑 ≥ 6.1毫米者皆為陽性。軸面短徑 < 6.1 毫米當中,如果淋巴結平均SUV ≥ 2.6 或者軸面長 ≥ 8毫米徑與冠面長徑 ≥ 25 毫米者還是屬於陽性,其餘還是陰性。單就正子斷層檢查而言,回復系數校正只有在 ≥ 6毫米才對鼻咽癌外側咽後淋巴結之診斷有幫助。和專家人為判斷的結果相比,類神經網路提供相當好且穩定的外側咽後淋巴結診斷。
英文摘要 Purpose: Identification of positive lateral retropharyngeal lymph (LRPL) nodes in nasopharyngeal carcinoma (NPC) is important. The primary objective of this study is to improve the diagnostic accuracy of LRPL node (LRPLN) with three parts. Part I is by determining the size range where the recovery coefficient (RC) method of fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) be helpful in detecting LRPL nodal metastases of NPC patients previously treated with radiation therapy. In part II, we use neural network (NN) to diagnose of LRPLNs automatically with parameters in PET/CT and magnetic resonance imaging (MRI). In part III, we explore other nodal parameters in MRI and PET/CT for increasing the prediction accuracy although minimal axial diameter (MIAD) in magnetic resonance imaging (MRI) has been recognized as the most useful parameter in diagnosing LRPLNs in NPC. Multi-stage approach with new diagnostic criterion for better accuracy and clinically was assessed.
Materials and Methods: The group I patient, a total of 142 LRPLNs assessed by MRI in 71 NPC patients was retrospectively chosen for partial volume correction study. LRPLNs with central necrosis, extracapsular invasion, or asymmetric grouping or those ascertained on follow-up MRI scans were considered positive for metastases. The criterion for positive diagnosis of nodal metastasis on FDG PET/CT scans was defined as maximal standard uptake value (SUVmax)  2.5. Nodes not separated from main tumors were excluded. An established RC method, the sphere-to-background ratio, was employed. Nodes were further categorized into three groups of minimal axial diameters: below 6 mm, 6 to 6.9 mm, and above 7 mm.
There was group II patient with totally 148 patients and 269 LRPLNs from China. These LRPLNs ascertained on follow-up MRI were considered positive for metastases. In part II and part III, both groups of patients and LPRLNs were used and a total of 411 LRPLNs were retrospectively collected from 219 patients with NPC. NN model was tested for 15 combinations of four parameters, namely MIAD, maximal axial diameter (MAAD), and maximal coronal diameter (MACD) and mean standard uptake value (NSUVmean). The optimal cutoff value of each parameter was derived for each parameter. The results of NN were compared with expert evaluation from 142 LRPLNs. The multi-stage approach for new criterion determined through was accessed through modified exhaustive search, and the new criteria were compared to single MIAD using a bootstrap sampling method.
Results: With RC method, a total of 88 separable LRPLNs were examined by FDG PET. Thirty-five nodes were positive and fifty-three nodes were negative. The RC method significantly improved sensitivity (from 20% to 100%) and accuracy (from 14% to 71%) for nodes sized 6 to 7 mm. The accuracy was improved from 92% to 96% for nodal size above 7 mm.
In NN and multi-step approach for new criterion, the accuracy rate (percentage) for the MIAD is 366/411 (89.1%). The optimal cutoff value is 6.0 mm. With four parameters, the accuracy rate was 89.09% for NN evaluation and for expert evaluation, 115/142 (81.0%), respectively. In predication, the optimal combinations of four parameters resulted in correct identification six additional nodes (372/411, 90.5%), representing 13.3% (6/45) decreases in incorrect prediction, respectively.
Conclusion: NPC LRPLNs with an MIAD ≥ 6.1 mm are positive. Among nodes with an MIAD < 6.1 mm, if the NSUVmean ≥ 2.6 or MACD ≥ 25 mm and MAAD ≥ 8 mm, the nodes are positive; otherwise, they are negative. Partial volume correction in PET/CT enhances the accuracy of detecting nodes in the size range of above 6 mm for LRPL nodal metastases of NPC. NN provides accurate and consistent diagnosis of LRPLNs comparable to expert judgment.
論文目次 Abstract V
中文摘要 VIII
致謝 X
Chapter 1. Introduction and Objectives 1
1.1 Background and Literature Review 1
1.1.1 Incidence of Nasopharyngeal Carcinoma 1
1.1.2 Spreading of Nasopharyngeal Carcinoma: The TNM staging 1
1.1.3 Curative Treatment for Nasopharyngeal Carcinoma 2
1.1.4 Significance of LRPL nodes 3
1.1.5 The Problems in LRPLNs Justification 4
1.1.6 Imaging for NPC 4
1.2 Motivations 18
1.3 Objectives 19
Chapter 2: PVC (measurement correction) using RC under the premise of correct nodal measurement (Objective 1) 20
2.1 Background 20
2.2 Methods and materials 20
2.2.1 Patients and LRPLNs (group I, from Taiwan) 20
2.2.2 Imaging studies in Taiwan 22
2.2.3 Image assessment in Taiwan 25
2.2.4 Diagnosis criteria for LRPLNs 28
2.2.5 Cutoff value as the diagnostic criteria for PET/CT before and after PVC 29
2.2.6 Cancer treatment: RT and chemotherapy in Taiwan 30
2.2.7Follow-up of LRPLNs in Taiwan 30
2.2.8 Excluded LRPLNs with possible spill-in effect 30
2.2.9 RC method for PVC 31
2.2.10 Diagnostic efficacy evaluation for PVC 34
2.3 Results 35
2.3.1 Results of assessment of LRPLNs for PVC 35
2.3.2 LRPLNs (group I) from Taiwan 35
2.3.3 LRPLNs for PVC after excluding spill-in node 36
2.3.4 Diagnostic efficacy evaluation for PVC 37
2.4 Discussions 39
2.4.1 The data compared to Zhang’s series 39
2.4.2 The clinical significant of the results of PVC 39
2.4.3 The comments for selected PVC method 40
2.4.4 Issues of selection of appropriate LRPLNs for PVC 41
2.4.5 Other limitations of PVC of detections for LRPL nodal metastasis 42
2.5 Conclusions 44
Chapter 3: Neural network (automated diagnosis, Objective I1) 45
3.1 Background 45
3.2 Methods and materials 45
3.2.1 Patients and LRPLNs (group II, from China) 46
3.2.2 Image assessment in China 47
3.2.3 Measuring SUVmean for Taiwan and China for NN study 47
3.2.4 Cancer treatment: RT and chemotherapy in China 48
3.2.6 NN model 48
3.2.7 The input parameters and division of NN 50
3.2.8 The pertinent steps of Matlab code 50
3.2.9 Counterpart evaluation of human expert for NN 52
3.2.10 Diagnostic efficacy evaluation of the optimal cutoff values of parameters 52
3.2.11 The NN efficacy evaluation and Statistical analysis for NN results 52
3.3 Results 53
3.3.1 LRPLNs (group II) in China 53
3.3.2 Total patients and LRPLNs from Taiwan and China 54
3.3.3 The accuracy results 54
3.4 Discussion 59
3.4.1 The pooling of two groups of patients and nodes 59
3.4.2 The efficacy of NN 59
3.4.3 The single parameter and multiple parameters NN modes 60
3.4.4 The limitation of NN and the future works 61
3.5 Conclusions 62
Chapter 4 Multi-stage approach for new criteria (Objective III) 63
4.1 Background 63
4.2 Methods and materials 63
4.2.1 Determining the best cutoff value of each single parameter. 63
4.2.2 Modified exhaustive search 64
4.2.3 Statistics for modified exhaustive search results analysis 65
4.3 Results of modified exhaustive search 66
4.3.1 Best cutoff value for single parameter 66
4.3.2 Multi-stage approaches for new criterion 67
4.3.3 Result of Bootstrap sampling 68
4.4 Discussions 69
4.4.1 Discussion of the best cutoff value for single parameter 69
4.4.2 Discussion of multi-stage approaches for new criterion and bootstrap sampling 70
4.4.3 The limitation of multi-stage approaches and future directions 71
Chapter 5 Total discussions and conclusions 72
5.1 Total discussion 72
5.1.1The clinical significance of accurate image diagnosis of LRPLNs in NPC 72
5.1.2 Lack of pathologic proof 72
5.1.3 The role of PVC in total dissertation 75
5.1.4 Metabolic volume and metabolic area in NN 75
5.1.5 The connection and comparison of efficacy for NN versus modified exhaustive searching 76
5.2 Final Conclusions 77
6. Reference 78
7. Appendix 89
Appendix I: Phantom study and raw data analysis 89
I.I Background 89
I.II Material and method: 89
I.III Results: Phantom study for spill-out and spill-in calculation 96
I-IV Discussion of the phantom study and image raw data analysis 104
Appendix II: Major Code of Matlab for NN 107
Appendix III: Results of NN in tables 113

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