請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80474完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
| dc.contributor.author | You-Wei Wang | en |
| dc.contributor.author | 王宥崴 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:07:24Z | - |
| dc.date.available | 2023-11-12 | |
| dc.date.available | 2022-11-24T03:07:24Z | - |
| dc.date.copyright | 2022-01-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-11-01 | |
| dc.identifier.citation | Reference Annema, J.T., van Meerbeeck, J.P., Rintoul, R.C., Dooms, C., Deschepper, E., Dekkers, O.M., De Leyn, P., Braun, J., Carroll, N.R., Praet, M., de Ryck, F., Vansteenkiste, J., Vermassen, F., Versteegh, M.I., Veseliç, M., Nicholson, A.G., Rabe, K.F., Tournoy, K.G., 2010. Mediastinoscopy vs endosonography for mediastinal nodal staging of lung cancer: a randomized trial. Jama 304, 2245-2252. Bach, P.B., Mirkin, J.N., Oliver, T.K., Azzoli, C.G., Berry, D.A., Brawley, O.W., Byers, T., Colditz, G.A., Gould, M.K., Jett, J.R., Sabichi, A.L., Smith-Bindman, R., Wood, D.E., Qaseem, A., Detterbeck, F.C., 2012. Benefits and Harms of CT Screening for Lung Cancer: A Systematic Review. Jama 307, 2418-2429. Bae, M.S., 2020. Using Deep Learning to Predict Axillary Lymph Node Metastasis from US Images of Breast Cancer. 294, 29-30. Burns, T.F., Rudin, C.M., 2010. Lung cancer in 'never-smokers': beyond EGFR mutations and EGFR-TK inhibitors. Oncology (Williston Park, N.Y.) 24, 48-49. Cambruzzi, E., Azeredo, A.M.d., Kronhart, A., Foltz, K.M., Zettler, C.G., Pêgas, K.L., 2014. The presence of metastases in regional lymph nodes is associated with tumor size and depth of invasion in sporadic gastric adenocarcinoma. Arq Bras Cir Dig 27, 18-21. Canziani, A., Paszke, A., Culurciello, E., 2016. An Analysis of Deep Neural Network Models for Practical Applications. CoRR abs/1605.07678. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H., 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Springer International Publishing, Cham, pp. 833-851. Coroller, T.P., Agrawal, V., Huynh, E., Narayan, V., Lee, S.W., Mak, R.H., Aerts, H., 2017. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 12, 467-476. Coroller, T.P., Agrawal, V., Narayan, V., Hou, Y., Grossmann, P., Lee, S.W., Mak, R.H., Aerts, H.J.W.L., 2016. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiotherapy and Oncology 119, 480-486. Coroller, T.P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R.T.H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R.H., Aerts, H.J.W.L., 2015. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiotherapy and Oncology 114, 345-350. Craigie, M., Squires, J., Miles, K., 2017. Can CT measures of tumour heterogeneity stratify risk for nodal metastasis in patients with non-small cell lung cancer? Clinical radiology 72, 899.e891-899.e897. Crinò, L., Weder, W., van Meerbeeck, J., Felip, E., 2010. Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology 21, v103-v115. Dou, T., Coroller, T., van Griethuysen, J., Mak, R., Aerts, H., 2018. Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLOS ONE 13, e0206108. Ebctcg, 2014. Effect of radiotherapy after mastectomy and axillary surgery on 10-year recurrence and 20-year breast cancer mortality: meta-analysis of individual patient data for 8135 women in 22 randomised trials. The Lancet 383, 2127-2135. Ettinger, D.S., Wood, D.E., Aisner, D.L., Akerley, W., Bauman, J., Chirieac, L.R., D'Amico, T.A., DeCamp, M.M., Dilling, T.J., Dobelbower, M., Doebele, R.C., Govindan, R., Gubens, M.A., Hennon, M., Horn, L., Komaki, R., Lackner, R.P., Lanuti, M., Leal, T.A., Leisch, L.J., Lilenbaum, R., Lin, J., Loo, B.W., Jr., Martins, R., Otterson, G.A., Reckamp, K., Riely, G.J., Schild, S.E., Shapiro, T.A., Stevenson, J., Swanson, S.J., Tauer, K., Yang, S.C., Gregory, K., Hughes, M., 2017. Non-Small Cell Lung Cancer, Version 5.2017, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network : JNCCN 15, 504-535. Feng, S.H., Yang, S.T., 2019. The new 8th TNM staging system of lung cancer and its potential imaging interpretation pitfalls and limitations with CT image demonstrations. Diagnostic and interventional radiology (Ankara, Turkey) 25, 270-279. Han, H., Chen, H., 2017. Selective lymph node dissection in early-stage non-small cell lung cancer. Journal of Thoracic Disease 9, 2102-2107. He, K., Zhang, X., Ren, S., Sun, J., 2015. Deep Residual Learning for Image Recognition. CoRR abs/1512.03385. Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-Excitation Networks, Computer Vision and Pattern Recognition, pp. 7132-7141. Huynh, E., Coroller, T.P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I., Mak, R.H., Aerts, H.J.W.L., 2016. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiotherapy and Oncology 120, 258-266. Ioffe, S., Szegedy, C., 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. JMLR.org, pp. 448–456. Kadota, K., Nitadori, J.-i., Sima, C.S., Ujiie, H., Rizk, N.P., Jones, D.R., Adusumilli, P.S., Travis, W.D., 2015a. Tumor Spread through Air Spaces is an Important Pattern of Invasion and Impacts the Frequency and Location of Recurrences after Limited Resection for Small Stage I Lung Adenocarcinomas. Journal of Thoracic Oncology 10, 806-814. Kadota, K., Yeh, Y.-C., Villena-Vargas, J., Cherkassky, L., Drill, E.N., Sima, C.S., Jones, D.R., Travis, W.D., Adusumilli, P.S., 2015b. Tumor Budding Correlates With the Protumor Immune Microenvironment and Is an Independent Prognostic Factor for Recurrence of Stage I Lung Adenocarcinoma. CHEST 148, 711-721. Kim, H.R., Shim, H.S., Chung, J.H., Lee, Y.J., Hong, Y.K., Rha, S.Y., Kim, S.H., Ha, S.J., Kim, S.K., Chung, K.Y., Soo, R., Kim, J.H., Cho, B.C., 2012. Distinct clinical features and outcomes in never-smokers with nonsmall cell lung cancer who harbor EGFR or KRAS mutations or ALK rearrangement. Cancer 118, 729-739. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. ImageNet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. Curran Associates Inc., Lake Tahoe, Nevada, pp. 1097-1105. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278-2324. Liu, B., Gao, S., Li, S., 2017. A Comprehensive Comparison of CT, MRI, Positron Emission Tomography or Positron Emission Tomography/CT, and Diffusion Weighted Imaging-MRI for Detecting the Lymph Nodes Metastases in Patients with Cervical Cancer: A Meta-Analysis Based on 67 Studies. Gynecologic and obstetric investigation 82, 209-222. Matsumura, Y., Ishii, G., Aokage, K., Kuwata, T., Hishida, T., Yoshida, J., Nishimura, M., Nagai, K., Ochiai, A., 2012. Morphophenotypic characteristics of intralymphatic cancer and stromal cells susceptible to lymphogenic metastasis. 103, 1342-1347. Nathanson, S.D., Rosso, K., Chitale, D., Burke, M., 2017. Chapter 13 - Lymph Node Metastasis∗∗Funded in part by the Nathanson/Rands Chair in Breast Cancer Research. Artwork by Kelly Rosso, MD, and Dhananjay Chitale, MD, In: Ahmad, A. (Ed.), Introduction to Cancer Metastasis. Academic Press, pp. 235-261. Oktay, O., Schlemper, J., Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N., Kainz, B., Glocker, B., Rueckert, D., 2018. Attention U-Net: Learning Where to Look for the Pancreas. Paleri, V., Urbano, T.G., Mehanna, H., Repanos, C., Lancaster, J., Roques, T., Patel, M., Sen, M., 2016. Management of neck metastases in head and neck cancer: United Kingdom National Multidisciplinary Guidelines. The Journal of Laryngology Otology 130, S161-S169. Popper, H.H., 2016. Progression and metastasis of lung cancer. Cancer and Metastasis Reviews 35, 75-91. Rössler, O., Betge, J., Harbaum, L., Mrak, K., Tschmelitsch, J., Langner, C., 2017. Tumor size, tumor location, and antitumor inflammatory response are associated with lymph node size in colorectal cancer patients. Modern Pathology 30, 897-904. Radosavovic, I., Prateek Kosaraju, R., Girshick, R., He, K., Dollár, P., 2020. Designing Network Design Spaces, Computer Vision and Pattern Recognition, pp. 10425-10433. Roy, M., Datta, A., 2019. Cancer Genetics and Therapeutics: Focus on Phytochemicals. Springer Singapore. Sanders, H.R., Albitar, M., 2010. Somatic mutations of signaling genes in non-small-cell lung cancer. Cancer genetics and cytogenetics 203, 7-15. Siegel, R., Ma, J., Zou, Z., Jemal, A., 2014. Cancer statistics, 2014. CA: a cancer journal for clinicians 64, 9-29. Siegel, R.L., Miller, K.D., Jemal, A., 2020. Cancer statistics, 2020. CA: a cancer journal for clinicians 70, 7-30. Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556. Sopik, V., Narod, S.A., 2018. The relationship between tumour size, nodal status and distant metastases: on the origins of breast cancer. Breast Cancer Res Treat 170, 647-656. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A., 2017. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI Press, pp. 4278–4284. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going Deeper with Convolutions, Computer Vision and Pattern Recognition, pp. 1-9. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016. Rethinking the Inception Architecture for Computer Vision, Computer Vision and Pattern Recognition, pp. 2818-2826. Uruga, H., Fujii, T., Fujimori, S., Kohno, T., Kishi, K., 2017. Semiquantitative Assessment of Tumor Spread through Air Spaces (STAS) in Early-Stage Lung Adenocarcinomas. Journal of Thoracic Oncology 12, 1046-1051. Wu, G.X., Raz, D.J., 2016. Lung Cancer Screening, In: Reckamp, K.L. (Ed.), Lung Cancer: Treatment and Research. Springer International Publishing, Cham, pp. 1-23. Wu, J., Aguilera, T., Shultz, D., Gudur, M., Rubin, D.L., Billy W. Loo, J., Diehn, M., Li, R., 2016. Early-Stage Non–Small Cell Lung Cancer: Quantitative Imaging Characteristics of 18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis. Radiology 281, 270-278. Zhang, J., Gold, K.A., Lin, H.Y., Swisher, S.G., Xing, Y., Lee, J.J., Kim, E.S., William, W.N., 2015. Relationship Between Tumor Size and Survival in Non–Small-Cell Lung Cancer (NSCLC): An Analysis of the Surveillance, Epidemiology, and End Results (SEER) Registry. Journal of Thoracic Oncology 10, 682-690. Zhang, T.-T., Qi, X.-Z., Chen, J.-P., Shi, R.-L., Wen, S.-S., Wang, Y.-L., Ji, Q.-H., Shen, Q., Zhu, Y.-X., Qu, N., 2019. The association between tumor’s location and cervical lymph nodes metastasis in papillary thyroid cancer. Gland Surgery 8, 557-568. Zhou, L.-Q., Wu, X.-L., Huang, S.-Y., Wu, G.-G., Ye, H.-R., Wei, Q., Bao, L.-Y., Deng, Y.-B., Li, X.-R., Cui, X.-W., Dietrich, C.F., 2020. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. Radiology 294, 19-28. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80474 | - |
| dc.description.abstract | "淋巴結轉移（nodal metastasis, Nmet）是醫生對於癌症診斷時的臨床主要任務，肺癌會依照腫瘤-淋巴結-轉移（Tumor-Node-Metastasis, TNM）報告來進行分期，其中淋巴結轉移與肺癌的生存和復發最為相關。目前來說，在手術前的淋巴轉移預測仍然是對患者管理手術計劃和做出治療決策的挑戰。對於這項挑戰我們提出了新的深度學習的預測方法，是具有腫瘤尺寸相關的模組來預測每個能階（單位為 keV）的影像和具有主要特徵增強模組多能階融合的深度學習模型，用於肺癌原發腫瘤的淋巴結轉移的識別，其中這些深度學習模型我們結合了放射科醫生和計算機科學的相關知識來進行淋巴轉移預測。在這篇文章中，所提出的深度學習模型是使用通過寶石光譜成像（gemstone spectral imaging, GSI）在肺癌原發性腫瘤的雙能量電腦斷層掃描（computer tomography, CT）上具有不同能階而定制設計的。在第一個部分中，使用每個能階的影像訓練出的最佳模型是由40能階影像數據集所訓練，淋巴轉移預測的準確度為86%，Kappa值為72%。第二部分，多個能階影像融合的最佳模型由較低能階影像的組合（40、50、60、70 keV）所訓練，達到93%的準確率和86%的Kappa值。在實驗中，我們有 11 張不同的單色影像從 40至140能階（間隔為10 keV），和三個不同能階融合數據組合於每位患者：較低能階組合，較高能能階組合（110、120、130、140 keV），以及平均能階的組合（40、70、100、140 keV）。當我們使用40能階影像訓練所提出的模型，發現與其他能階在預測淋巴結轉移有顯著差異，並利用交叉驗證（cross-validation）來解釋較低的能階影像在預測原發腫瘤的淋巴結轉移更有效。當我們使用較低能階影像的組合訓練融合模型時，發現與放射科醫生有顯著差異，準確度更接近病理報告。因此，在多能階組合的部分，我們也使用交叉驗證來證明較低能階融合模型更加穩定，更適合用來從原發性腫瘤預測淋巴結轉移。結果表明，較低能階的雙能量電腦斷層影像顯示出更多的腫瘤血管生成與異質性，這些特徵提供我們所設計的深度學習模型用來預測淋巴結轉移。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:07:24Z (GMT). No. of bitstreams: 1 U0001-2610202117593900.pdf: 7776681 bytes, checksum: 199376642f9301993af77c0f6e805345 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "Table of Contents 口試委員會審定書....................................................................I ACKNOWLEGEGMENTS..................................................................II 摘要..............................................................................III ABSTRACT..........................................................................V Table of Contents.................................................................VII List of Figures...................................................................X List of Tables....................................................................XIV Chapter 1. Introduction........................................................1 1.1 Background and Motivation...................................................1 1.2 Research Aims and Objectives................................................3 Chapter 2. Related Work........................................................5 2.1 An Overview of Lymph Node Metastasis........................................5 2.2 The Relationship Between Nodal Metastasis, Tumor Size and Depth of Invasion.6 2.3 Classification of Deep Learning Model.......................................7 2.4 The Prediction of Lymph Node Metastasis from Primary Tumor.................12 Chapter 3. Materials and Methods..............................................14 3.1 Materials..................................................................15 3.2 The Proposed Method........................................................20 3.3 Nodal Metastasis Prediction using Different Energy Level Image.............23 3.3.1 Patch Selection............................................................24 3.3.2 Image Representation.......................................................26 3.3.3 Damper Block...............................................................32 3.4 Nodal Metastasis Prediction using Multi-Energy Level Image.................34 3.4.1 Core-ring Blocks Residual Estimation with Size-related Damper Block........34 3.4.2 Principal Feature Enhancement Block........................................36 3.4.3 Squeeze-and-Excitation Block...............................................40 Chapter 4. Experiment.........................................................42 4.1 Accuracy of Nodal Stage Prediction with Different Energy Level.............44 4.2 Comparison of The Proposed Model using per keV image with Different Module.48 4.3 The Proposed Model with Cross-Validation in Pathology Set..................57 4.4 Comparison of The Proposed Fusion Model with and without PFE block.........60 4.5 The Proposed Fusion Model with Cross-Validation in Pathology Set...........69 Chapter 5. Discussion.........................................................72 Chapter 6. Conclusion.........................................................77 Appendix...........................................................................81 Reference..........................................................................83" | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 淋巴結轉移 | zh_TW |
| dc.subject | 原發性肺腫瘤 | zh_TW |
| dc.subject | 雙能量電腦斷層影像 | zh_TW |
| dc.subject | Dual Energy CT | en |
| dc.subject | Lymph Node Metastasis | en |
| dc.subject | Nodal Metastasis | en |
| dc.subject | Primary Lung Tumor | en |
| dc.subject | Deep Learning | en |
| dc.title | 使用深度學習對雙能量肺部電腦斷層影像進行淋巴結轉移預測 | zh_TW |
| dc.title | Nodal Metastasis Prediction on Dual Energy Lung CT Image using Deep Learning | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 傅楸善(Hsin-Tsai Liu),張傑帆(Chih-Yang Tseng),陳啟禎,羅崇銘 | |
| dc.subject.keyword | 淋巴結轉移,原發性肺腫瘤,雙能量電腦斷層影像,深度學習, | zh_TW |
| dc.subject.keyword | Lymph Node Metastasis,Nodal Metastasis,Primary Lung Tumor,Dual Energy CT,Deep Learning, | en |
| dc.relation.page | 91 | |
| dc.identifier.doi | 10.6342/NTU202104263 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-11-02 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-11-12 | - |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2610202117593900.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 7.59 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。