請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90629
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 莊曜宇 | zh_TW |
dc.contributor.advisor | Eric Y. Chuang | en |
dc.contributor.author | 陳翰儒 | zh_TW |
dc.contributor.author | Han-Ru Chen | en |
dc.date.accessioned | 2023-10-03T16:55:55Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-10-03 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-07 | - |
dc.identifier.citation | 1. Louis, D.N., et al., Computational pathology: an emerging definition. Archives of Pathology & Laboratory Medicine, 2014. 138(9): p. 1133-1138.
2. Abels, E., et al., Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. The Journal of Pathology, 2019. 249(3): p. 286-294. 3. Jahn, S.W., M. Plass, and F. Moinfar, Digital pathology: advantages, limitations and emerging perspectives. Journal of Clinical Medicine, 2020. 9(11): p. 3697. 4. Fuchs, T.J. and J.M. Buhmann, Computational pathology: challenges and promises for tissue analysis. Computerized Medical Imaging and Graphics, 2011. 35(7-8): p. 515-530. 5. Salvi, M., et al., The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine, 2021. 128: p. 104129. 6. Schmauch, B., et al., A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nature Communications, 2020. 11(1): p. 3877. 7. Alsaafin, A., et al., Learning to predict RNA sequence expressions from whole slide images with applications for search and classification. Communications Biology, 2023. 6(1): p. 304. 8. Wang, Y., et al., Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression–morphology analysis in breast cancer. Cancer Research, 2021. 81(19): p. 5115-5126. 9. He, B., et al., Integrating spatial gene expression and breast tumour morphology via deep learning. Nature Biomedical Engineering, 2020. 4(8): p. 827-834. 10. Levy-Jurgenson, A., et al., Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Scientific Reports, 2020. 10(1): p. 1-11. 11. Qu, H., et al., Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. NPJ Precision Oncology, 2021. 5(1): p. 87. 12. Campanella, G., et al., Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 2019. 25(8): p. 1301-1309. 13. Xu, S., et al., Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer. BMC Medical Genomics, 2020. 13(11): p. 1-12. 14. Hao, J., et al. PAGE-Net: interpretable and integrative deep learning for survival analysis using histopathological images and genomic data. in Pacific Symposium on Biocomputing 2020. 2019. World Scientific. 15. Chen, R.J., et al., Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Transactions on Medical Imaging, 2020. 41(4): p. 757-770. 16. Ning, Z., et al., Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma. Bioinformatics, 2020. 36(9): p. 2888-2895. 17. Cheerla, A. and O. Gevaert, Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics, 2019. 35(14): p. i446-i454. 18. Mobadersany, P., et al., Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 2018. 115(13): p. E2970-E2979. 19. Kather, J.N., et al., Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine, 2019. 16(1): p. e1002730. 20. Phan, N.N., et al., Predicting breast cancer gene expression signature by applying deep convolutional neural networks from unannotated pathological images. Frontiers in Oncology, 2021. 11: p. 769447. 21. Phan, N.N., et al., Prediction of breast cancer recurrence using a deep convolutional neural network without region-of-interest labeling. Frontiers in Oncology, 2021: p. 4274. 22. Bae, S., H. Choi, and D.S. Lee, Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images. Nucleic Acids Research, 2021. 49(10): p. e55-e55. 23. Tan, X., et al., SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics, 2019. 36(7): p. 2293-2294. 24. Schneider, L., et al., Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. European Journal of Cancer, 2022. 160: p. 80-91. 25. Ongsulee, P. Artificial intelligence, machine learning and deep learning. in 2017 15th international conference on ICT and knowledge engineering (ICT&KE). 2017. IEEE. 26. Nasteski, V., An overview of the supervised machine learning methods. Horizons. b, 2017. 4: p. 51-62. 27. Sathya, R. and A. Abraham, Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence, 2013. 2(2): p. 34-38. 28. LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. Nature, 2015. 521(7553): p. 436-444. 29. Rosenblatt, F., The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 1958. 65(6): p. 386. 30. Shrestha, A. and A. Mahmood, Review of deep learning algorithms and architectures. IEEE Access, 2019. 7: p. 53040-53065. 31. Ray Bernard PSP, C.-I. Deep Learning to the Rescue. 2019 [cited 2023 May 21]; Available from: https://www.go-rbcs.com/columns/deep-learning-to-the-rescue. 32. Roos, M. Deep Learning Neurons versus Biological Neurons. 2019 [cited 2023 May 21]; Available from: https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9. 33. Sutskever, I., et al. On the importance of initialization and momentum in deep learning. in International conference on machine learning. 2013. PMLR. 34. Kingma, D.P. and J. Ba, Adam: A method for stochastic optimization. arXiv Preprint arXiv:1412.6980, 2014. 35. Loshchilov, I. and F. Hutter, Decoupled weight decay regularization. arXiv Preprint arXiv:1711.05101, 2017. 36. Ruder, S., An overview of gradient descent optimization algorithms. arXiv Preprint arXiv:1609.04747, 2016. 37. Dalianis, H. and H. Dalianis, Evaluation metrics and evaluation. Clinical Text Mining: Secondary Use of Electronic Patient Records, 2018: p. 45-53. 38. LeCun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11): p. 2278-2324. 39. E, S.K. Convolutional Neural Network | Deep Learning. 2020 [cited 2023 May 22]; Available from: https://developersbreach.com/convolution-neural-network-deep-learning/. 40. O'Shea, K. and R. Nash, An introduction to convolutional neural networks. arXiv Preprint arXiv:1511.08458, 2015. 41. Student Notes: Convolutional Neural Networks (CNN) Introduction. 2018 [cited 2023 May 22]; Available from: https://indoml.com/2018/03/07/student-notes-convolutional-neural-networks-cnn-introduction/. 42. Brownlee, J. How to Visualize Filters and Feature Maps in Convolutional Neural Networks. Deep Learning for Computer Vision 2019 [cited 2023 May 22]; Available from: https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/. 43. Deng, J., et al. Imagenet: A large-scale hierarchical image database. in 2009 IEEE conference on computer vision and pattern recognition. 2009. IEEE. 44. Ajit, A., K. Acharya, and A. Samanta. A review of convolutional neural networks. in 2020 international conference on emerging trends in information technology and engineering (ic-ETITE). 2020. IEEE. 45. Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Communications of the ACM, 2017. 60(6): p. 84-90. 46. Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556, 2014. 47. Szegedy, C., et al. Inception-v4, inception-resnet and the impact of residual connections on learning. in Proceedings of the AAAI Conference on Artificial Intelligence. 2017. 48. Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. 49. He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. 50. Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. 51. Chollet, F. Xception: Deep learning with depthwise separable convolutions. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. 52. Tan, M. and Q. Le. Efficientnet: Rethinking model scaling for convolutional neural networks. in International Conference on Machine Learning. 2019. PMLR. 53. Liu, Z., et al. A convnet for the 2020s. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022. 54. Bianco, S., et al., Benchmark analysis of representative deep neural network architectures. IEEE Access, 2018. 6: p. 64270-64277. 55. Maron, O. and T. Lozano-Pérez, A framework for multiple-instance learning. Advances in Neural Information Processing Systems, 1997. 10. 56. Carbonneau, M.-A., et al., Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognition, 2018. 77: p. 329-353. 57. Kumar, J., J. Pillai, and D. Doermann. Document image classification and labeling using multiple instance learning. in 2011 International Conference on Document Analysis and Recognition. 2011. IEEE. 58. Dietterich, T.G., R.H. Lathrop, and T. Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 1997. 89(1-2): p. 31-71. 59. Quellec, G., et al., Multiple-instance learning for medical image and video analysis. IEEE Reviews in Biomedical Engineering, 2017. 10: p. 213-234. 60. Vaswani, A., et al., Attention is all you need. Advances in Neural Information Processing Systems, 2017. 30. 61. Xu, K., et al. Show, attend and tell: Neural image caption generation with visual attention. in International Conference on Machine Learning. 2015. PMLR. 62. Gregor, K., et al. Draw: A recurrent neural network for image generation. in International conference on machine learning. 2015. PMLR. 63. Dosovitskiy, A., et al., An image is worth 16x16 words: Transformers for image recognition at scale. arXiv Preprint arXiv:2010.11929, 2020. 64. Bahdanau, D., K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate. arXiv Preprint arXiv:1409.0473, 2014. 65. Ilse, M., J. Tomczak, and M. Welling. Attention-based deep multiple instance learning. in International Conference on Machine Learning. 2018. PMLR. 66. Hulsen, T., Literature analysis of artificial intelligence in biomedicine. 2021. 67. Castiglioni, I., et al., AI applications to medical images: From machine learning to deep learning. Physica Medica, 2021. 83: p. 9-24. 68. Yu, K.-H., A.L. Beam, and I.S. Kohane, Artificial intelligence in healthcare. Nature Biomedical Engineering, 2018. 2(10): p. 719-731. 69. Dhivya, S., S.H. Priya, and R. Sathishkumar, Artificial intelligence in systems biology: opportunities in agriculture, biomedicine, and healthcare, in Artificial Intelligence Theory, Models, and Applications. 2021, Auerbach Publications. p. 121-142. 70. Secinaro, S., et al., The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 2021. 21: p. 1-23. 71. Rong, G., et al., Artificial intelligence in healthcare: review and prediction case studies. Engineering, 2020. 6(3): p. 291-301. 72. Macenko, M., et al. A method for normalizing histology slides for quantitative analysis. in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009. IEEE. 73. Olsen, U.D.J.G.I.H.T.B.E.P.P.R.P.W.S.S.T.D.N.C.J.-F., et al., Initial sequencing and analysis of the human genome. Nature, 2001. 409(6822): p. 860-921. 74. Cindy Sampias, G.R. H&E Staining Overview: A Guide to Best Practices. 2023 [cited 2023 May 2]; Available from: https://www.leicabiosystems.com/knowledge-pathway/he-staining-overview-a-guide-to-best-practices/. 75. Vaske, C.J., et al., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 2010. 26(12): p. i237-i245. 76. Hoadley, K.A., et al., Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 2018. 173(2): p. 291-304. e6. 77. Goldman, M.J., et al., Visualizing and interpreting cancer genomics data via the Xena platform. Nature Biotechnology, 2020. 38(6): p. 675-678. 78. Rouillard, A.D., et al., The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database, 2016. 2016. 79. Ashburner, M., et al., Gene ontology: tool for the unification of biology. Nature Genetics, 2000. 25(1): p. 25-29. 80. Mi, H., et al., PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Research, 2019. 47(D1): p. D419-D426. 81. Jassal, B., et al., The reactome pathway knowledgebase. Nucleic Acids Research, 2020. 48(D1): p. D498-D503. 82. Nishimura, D., BioCarta. Biotech Software & Internet Report: The Computer Software Journal for Scient, 2001. 2(3): p. 117-120. 83. Schaefer, C.F., et al., PID: the pathway interaction database. Nucleic Acids Research, 2009. 37(suppl_1): p. D674-D679. 84. Loeliger, H.-A., An introduction to factor graphs. IEEE Signal Processing Magazine, 2004. 21(1): p. 28-41. 85. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research, 2021. 49(D1): p. D325-D334. 86. Otsu, N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979. 9(1): p. 62-66. 87. Bangare, S.L., et al., Reviewing Otsu’s method for image thresholding. International Journal of Applied Engineering Research, 2015. 10(9): p. 21777-21783. 88. Roy, S., et al., A study about color normalization methods for histopathology images. Micron, 2018. 114: p. 42-61. 89. Bansal, R., G. Raj, and T. Choudhury. Blur image detection using Laplacian operator and Open-CV. in 2016 International Conference System Modeling & Advancement in Research Trends (SMART). 2016. IEEE. 90. Van Vliet, L.J., I.T. Young, and G.L. Beckers, A nonlinear Laplace operator as edge detector in noisy images. Computer Vision, Graphics, and Image Processing, 1989. 45(2): p. 167-195. 91. Fremond, S., et al., Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts. The Lancet Digital Health, 2023. 5(2): p. e71-e82. 92. Lin, M., Q. Chen, and S. Yan, Network in network. arXiv Preprint arXiv:1312.4400, 2013. 93. Rastogi, A. ResNet50. 2022 [cited 2023 May 23]; Available from: https://blog.devgenius.io/resnet50-6b42934db431. 94. Li, Z., et al., A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 2021. 95. Muñoz-Aguirre, M., et al., PyHIST: a histological image segmentation tool. PLoS Computational Biology, 2020. 16(10): p. e1008349. 96. Carpenter, A.E., et al., CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology, 2006. 7: p. 1-11. 97. Stirling, D.R., et al., CellProfiler 4: improvements in speed, utility and usability. BMC Bioinformatics, 2021. 22: p. 1-11. 98. Kaplan, E.L. and P. Meier, Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 1958. 53(282): p. 457-481. 99. Schömig-Markiefka, B., et al., Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern Pathology, 2021. 34(12): p. 2098-2108. 100. Wang, N.C., et al., Stress testing pathology models with generated artifacts. Journal of Pathology Informatics, 2021. 12(1): p. 54. 101. Yang, L., et al., FGF/FGFR signaling: From lung development to respiratory diseases. Cytokine & Growth Factor Reviews, 2021. 62: p. 94-104. 102. Li, Y., et al., The multifaceted roles of FOXM1 in pulmonary disease. Cell Communication and Signaling, 2019. 17(1): p. 1-16. 103. Liao, G.-B., et al., Regulation of the master regulator FOXM1 in cancer. Cell Communication and Signaling, 2018. 16(1): p. 57. 104. Yang, X., et al., Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR. Experimental and Therapeutic Medicine, 2023. 25(1): p. 1-14. 105. Ahmed, F., Integrated network analysis reveals FOXM1 and MYBL2 as key regulators of cell proliferation in non-small cell lung cancer. Frontiers in Oncology, 2019. 9: p. 1011. 106. Kurubanjerdjit, N. and K.-L. Ng, A database of integrated molecular and phytochemical interactions of the foxm1 pathway for lung cancer. Journal of Biomolecular Structure and Dynamics, 2022. 40(1): p. 177-189. 107. Kent, L.N. and G. Leone, The broken cycle: E2F dysfunction in cancer. Nature Reviews Cancer, 2019. 19(6): p. 326-338. 108. Gorgoulis, V.G., et al., Transcription factor E2F‐1 acts as a growth‐promoting factor and is associated with adverse prognosis in non‐small cell lung carcinomas. The Journal of Pathology, 2002. 198(2): p. 142-156. 109. Du, K., et al., E2F2 promotes lung adenocarcinoma progression through B-Myb-and FOXM1-facilitated core transcription regulatory circuitry. International Journal of Biological Sciences, 2022. 18(10): p. 4151-4170. 110. Shin, S.-B., et al., Active PLK1-driven metastasis is amplified by TGF-β signaling that forms a positive feedback loop in non-small cell lung cancer. Oncogene, 2020. 39(4): p. 767-785. 111. Iliaki, S., R. Beyaert, and I.S. Afonina, Polo-like kinase 1 (PLK1) signaling in cancer and beyond. Biochemical Pharmacology, 2021. 193: p. 114747. 112. Jang, H.-R., et al., PLK1/vimentin signaling facilitates immune escape by recruiting Smad2/3 to PD-L1 promoter in metastatic lung adenocarcinoma. Cell Death & Differentiation, 2021. 28(9): p. 2745-2764. 113. Miyazono, K., S. Maeda, and T. Imamura, BMP receptor signaling: transcriptional targets, regulation of signals, and signaling cross-talk. Cytokine & Growth Factor Reviews, 2005. 16(3): p. 251-263. 114. Wu, C.-K., et al., BMP2 promotes lung adenocarcinoma metastasis through BMP receptor 2-mediated SMAD1/5 activation. Scientific Reports, 2022. 12(1): p. 16310. 115. Zygalaki, E., et al., Quantitative real-time reverse transcription–PCR study of the expression of vascular endothelial growth factor (VEGF) splice variants and VEGF receptors (VEGFR-1 and VEGFR-2) in non–small cell lung cancer. Clinical Chemistry, 2007. 53(8): p. 1433-1439. 116. Melincovici, C.S., et al., Vascular endothelial growth factor (VEGF)-key factor in normal and pathological angiogenesis. Rom J Morphol Embryol, 2018. 59(2): p. 455-467. 117. Du, X., et al., ALK‐rearrangement in non‐small‐cell lung cancer (NSCLC). Thoracic Cancer, 2018. 9(4): p. 423-430. 118. Młynarczyk, G., et al., Grade‐dependent changes in sphingolipid metabolism in clear cell renal cell carcinoma. Journal of Cellular Biochemistry, 2022. 123(4): p. 819-829. 119. Natoli, T.A., V. Modur, and O. Ibraghimov-Beskrovnaya, Glycosphingolipid metabolism and polycystic kidney disease. Cellular Signalling, 2020. 69: p. 109526. 120. Afshar-Kharghan, V., The role of the complement system in cancer. The Journal of Clinical Investigation, 2017. 127(3): p. 780-789. 121. Roumenina, L.T., et al., Tumor cells hijack macrophage-produced complement C1q to promote tumor GrowthIntratumoral complement promotes cancer progression. Cancer Immunology Research, 2019. 7(7): p. 1091-1105. 122. Yang, X., et al., A pan-cancer analysis of the HER family gene and their association with prognosis, tumor microenvironment, and therapeutic targets. Life Sciences, 2021. 273: p. 119307. 123. Feng, D., et al., Identification of a novel nomogram to predict progression based on the circadian clock and insights into the tumor immune microenvironment in prostate cancer. Frontiers in Immunology, 2022. 13: p. 123. 124. Wendeu‐Foyet, M.G., et al., Circadian genes and risk of prostate cancer: Findings from the EPICAP study. International Journal of Cancer, 2019. 145(7): p. 1745-1753. 125. Jiramongkol, Y. and E.W.-F. Lam, FOXO transcription factor family in cancer and metastasis. Cancer and Metastasis Reviews, 2020. 39: p. 681-709. 126. Habrowska-Górczyńska, D.E., et al., FOXO3a and its regulators in prostate cancer. International Journal of Molecular Sciences, 2021. 22(22): p. 12530. 127. Farhan, M., et al., FOXO signaling pathways as therapeutic targets in cancer. International Journal of Biological Sciences, 2017. 13(7): p. 815. 128. Casalou, C., A. Ferreira, and D.C. Barral, The role of ARF family proteins and their regulators and effectors in cancer progression: a therapeutic perspective. Frontiers in Cell and Developmental Biology, 2020. 8: p. 217. 129. Lang, L., et al., Combined targeting of Arf1 and Ras potentiates anticancer activity for prostate cancer therapeutics. Journal of Experimental & Clinical Cancer Research, 2017. 36(1): p. 1-10. 130. Davis, J.E., et al., ARF1 promotes prostate tumorigenesis via targeting oncogenic MAPK signaling. Oncotarget, 2016. 7(26): p. 39834. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90629 | - |
dc.description.abstract | 鑑定癌症特定的生物標記對精準醫學的發展至關重要。近年來,整合高通量資料與生物資訊分析方法的研究在定序技術以及演算法的發展下蓬勃發展,改變了生醫研究的樣貌。然而,在計算病理學領域中,對於分析組織切片影像和了解其中分子層級的資訊仍存在許多未知因素。為了填補此一空洞並解開與這些影像相關的潛在路徑與多體學標記,本研究提出了一個新的分析流程。此流程整合多種高通量資料並利用深度學習模型來預測癌症特定的路徑以及多體學標記,並延伸進行多種分析,有助於更加深入瞭解組織切片影像中的癌症生物學。
本研究分析了肺腺癌、肺鱗狀細胞癌、腎臟透明細胞瘤以及攝護腺癌的全切片影像、PARADIGM路徑表現資訊、基因表現、基因層級拷貝數資料。全切片影像在前處理時會經過補丁生成、顏色正規化、模糊補丁移除、聚類和特徵提取。採用注意力機制的深度學習模型被訓練用於預測每種癌症的路徑標記,並同時生成注意力權重的熱圖來提升模型解釋性。路徑標記的標準為Spearman相關係數的p值並經過 Bonferroni校正來鑑定。進行影像形態學分析時利用支持向量機關係係數對組織切片影像重要區域進行分析,找出與路徑表現相關之影像形態特徵。接著本研究使用額外的深度學習模型與路徑標記相關的基因資料預測基因表現及拷貝數標記。本研究以存活分析方法探討所預測之標記是否具備作為潛在預後因子的能力,且結果顯示大多數預測結果在高風險及低風險樣本之間達到統計上的顯著差異。最後為檢驗此分析流程及結果的穩定性和穩健性,本研究使用模擬的資料對分析流程進行壓力測試。此一完整分析流程整合各種類型的資料和人工智慧模型,揭示了不同癌症的特定生物標記,並對影像中分子層級的資訊與其潛在的預後價值提供了新的認知。 總結來說,本研究提出了一個全新的分析流程利用全切片影像來預測潛在的路徑以及多體學標記。此研究亦呈現了全面的分析結果,展示了這些生物標記的重要性。值得一提的是,此方法為組織切片影像中預篩選潛在的路徑以及多體學圖譜提供了一個具成本效益的解決方案,為癌症研究和精準醫學提供了有價值的見解。 | zh_TW |
dc.description.abstract | The identification of cancer-specific signatures or biomarkers is crucial for advancing precision medicine. Recent advancements in sequencing technologies and computational algorithms have paved the way for integrating high-throughput data and computational approaches, revolutionizing biomedical research. However, in the field of computational pathology, a gap still exists between understanding the molecular mechanisms and analyzing histopathological images. Therefore, this study proposed an analytical pipeline to bridge this gap and unraveled the underlying pathways and multi-omics signatures associated with these images. This pipeline integrated multiple data modalities and utilized attention-based deep learning models to discover cancer-specific pathways and multi-omics signatures and conducted several extended analyses, contributing to a deeper understanding of cancer biology in histopathological images.
This study analyzed whole slide images (WSIs), PARADIGM pathway activities, RNA-seq expression, and gene-level copy number data from lung adenocarcinoma, lung squamous cell carcinoma, clear cell renal carcinoma, and prostate adenocarcinoma. The WSIs underwent preprocessing steps including patch generation, color normalization, blurry patch removal, clustering, and feature extraction. The attention-based models were trained to predict pathway signatures for each cancer type, with attention weight heatmaps aiding interpretation. Pathway signatures were determined based on Bonferroni-corrected p-values of Spearman correlation. Morphology analysis was conducted on important WSI regions indicated by attention weights using linear-kernel support vector machine coefficients to find the important morphological feature for the activation of the pathway. Genes associated with the pathway signatures were extracted, and additional attention-based models were trained to predict RNA-seq expression and gene-level copy number signatures. Survival analysis and log-rank tests examined the prognostic value of predicted signatures and most of the results showed significant differences between high-risk and low-risk groups. Stress testing was performed using mixed simulated and original data to assess pipeline robustness. This comprehensive pipeline integrated various data types and artificial intelligence models to uncover cancer-specific signatures, shedding light on molecular characteristics and potential prognostic implications across different cancer types. In summary, this study proposed an analytic pipeline to predict underlying pathway and multi-omics signatures using WSI. The study also presented comprehensive analysis results to showcase the significance of these signatures. Notably, this approach offered a cost-effective solution for pre-screening potential pathway and multi-omics profiles in histopathological images, providing valuable insights for cancer research and precision medicine. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:55:55Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-10-03T16:55:55Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 I
中文摘要 II Abstract IV Content VI List of Abbreviations IX List of Tables XI List of Figures XII Chapter 1. Introduction 1 1.1 Digital Pathology and Computational Pathology 1 1.2 Multi-Omics Data Integration for Cancer 3 1.3 Artificial Intelligence 5 1.3.1 Machine Learning 6 1.3.2 Deep Neural Network 7 1.3.3 Deep Neural Network Development 9 1.3.4 Convolutional Neural Network 11 1.3.5 Multiple Instance Learning 14 1.3.6 Attention-Based Deep Learning 16 1.3.7 Artificial Intelligence in Biomedicine 18 1.4 Motivation 20 1.5 Specific Aims 21 Chapter 2. Materials and Methods 25 2.1 General Description of the Proposed Analytic Pipeline 25 2.2 Materials 27 2.2.1 Whole Slide Images 29 2.2.2 PARADIGM Pathway Activities 31 2.2.3 RNA-seq Expression 33 2.2.4 Gene-Level Copy Number 34 2.3 Databases 34 2.3.1 TCGA GDC Data Portal 35 2.3.2 UCSC Xena 35 2.3.3 Harmonizome 36 2.3.4 Gene Ontology 37 2.4 Whole Slide Images Preprocessing 37 2.4.1 Patch Generation 37 2.4.2 Color Normalization 40 2.4.3 Blurry Patches Removal 41 2.4.4 Feature Extraction 43 2.4.5 K-Means Clustering 44 2.5 CNN Models 47 2.5.1 ResNet50 47 2.5.2 Densenet121 48 2.5.3 Inception Resnet v2 48 2.6 Attention-Based Deep Learning Models 51 2.7 Model Development 54 2.7.1 Training-Validation-Testing Splitting 54 2.7.2 5-Fold Cross-Validation 55 2.7.3 Hyperparameters Setting 57 2.7.4 Hardware and Software Environment 58 2.8 Signature Identification 58 2.9 Attention Weight Heatmaps Visualization 59 2.10 Morphology Analysis 60 2.11 Gene Ontology Enrichment Analysis 62 2.12 Prognostic Refinement 62 2.13 Stress Testing 63 Chapter 3. Results 69 3.1 Predicted Pathway Signatures 69 3.2 Attention Weight Heatmaps 74 3.3 Morphology Analysis 77 3.4 Predicted Multi-Omics Signatures 85 3.5 Gene Ontology Enrichment Analysis 86 3.6 Prognostic Refinement 88 3.7 Stress Testing 92 3.8 RNA-seq Expression Prediction Performance Comparison 96 Chapter 4. Discussion 101 4.1 Cancer-Related Signatures 103 4.2 Limitation 109 Chapter 5. Conclusion 111 References 113 Supplementary Materials 123 | - |
dc.language.iso | en | - |
dc.title | 以注意力機制深度學習用於病理切片影像之路徑及體學標記整合分析 | zh_TW |
dc.title | Integrative Analysis of Pathway-Omics Signature in Histopathological Images via Attention-Based Deep Learning | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 賴亮全;盧子彬;黄宇飞 | zh_TW |
dc.contributor.oralexamcommittee | Liang-Chuan Lai;Tzu-Pin Lu;Yufei Huang | en |
dc.subject.keyword | 計算病理學,病理組織切片影像,路徑標記,多體學標記,注意力機制深度學習模型, | zh_TW |
dc.subject.keyword | computational pathology,histopathological images,pathway signature,multi-omics signature,attention-based deep learning, | en |
dc.relation.page | 147 | - |
dc.identifier.doi | 10.6342/NTU202303394 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-11 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
顯示於系所單位: | 生醫電子與資訊學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 77.71 MB | Adobe PDF | 檢視/開啟 |
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。