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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54117完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王偉仲(Weichung Wang) | |
| dc.contributor.author | Yuehchou Lee | en |
| dc.contributor.author | 李岳洲 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:40:42Z | - |
| dc.date.available | 2022-08-31 | |
| dc.date.copyright | 2020-08-07 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-04 | |
| dc.identifier.citation | [1] Ackerman LV. Computed tomography scans on paper. Radiology, Sep 1978. [2] Adam G. Johnson, Jimmy Ruiz, Ryan Hughes, et al. Impact of systemic targeted agents on the clinical outcomes of patients with brain metastases. Oncotarget, Aug 2015. [3] Andrew P.Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, July 1997. [4] Dana Greene-Schloesser, Mike E. Robbins, Ann M. Peiffer, et al. Radiation-induced brain injury: A review. Front Oncol, Jul 2012. [5] David Liljequist, Britt Elfving, and Kirsti S. Roaldsen. Intraclass correlation –A discussion and demonstration of basic features. PLoS One, July 2019. [6] Dragos D. Margineantu and Thomas G. Dietterich. Bootstrap Methods for the Cost- Sensitive Evaluation of Classifiers. 2000. [7] Eric L Chang, Jeffrey S Wefel, Kenneth R Hess, et al. Neurocognition in Patients With Brain Metastases Treated With Radiosurgery or Radiosurgery Plus Whole- Brain Irradiation: A Randomised Controlled Trial. Lancet Oncol, Nov 2009. [8] G Minniti, M Salvati, R Muni, et al. Stereotactic radiosurgery plus whole-brain radiation therapy vs stereotactic radiosurgery alone for treatment of brain metastases: a randomized controlled trial. Anticancer Res, Jul 2010. [9] Hidefumi Aoyama, Masao Tago, Hiroki Shirato, et al. Stereotactic Radiosurgery With or Without Whole-Brain Radiotherapy for Brain Metastases: Secondary Anal- ysis of the JROSG 99-1 Randomized Clinical Trial. JAMA Oncol, Jul 2015. [10] Jerome H Friedman. Greedy Function Approximation: A Gradient Boosting Ma- chine. The Annals of Statistics, 29(5), 2001. [11] Jerome H Friedman. Stochastic Gradient Boosting. Computational Statistics and Data Analysis, 38, Feb 2002. [12] John J. Bartko. The Intraclass Correlation Coefficient as a Measure of Reliability. SAGE, Aug 1966. [13] Koch GG. Intraclass correlation coefficient. Encyclopedia of statistical sciences, 2004. [14] Lambin P, Leijenaar RTH, Deist TM, and et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017. [15] Lambin P, Rios-Velazquez E, Leijenaar R, and et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, Mar 2012. [16] G. H. Laurens van der Maaten. Visualizing data using t-sne. Journal of Machine Learning Research, 9:2579–2605, 2008. [17] Linden A. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J Eval Clin Pract, Apr 2006. [18] Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classification tasks. Information Processing and Management, May 2009. [19] Riccardo Soffietti, Martin Kocher, Ufuk M Abacioglu, et al. A European Organi- sation for Research and Treatment of Cancer phase III trial of adjuvant whole-brain radiotherapy versus observation in patients with one to three brain metastases from solid tumors after surgical resection or radiosurgery: quality-of-life results. J Clin Oncol., Jan 2013. [20] Roger J. Lewis. An Introduction to Classification and Regression Tree (CART) Anal- ysis. Annual Meeting of the Society for Academic Emergency Medicine in San Fran- cisco, 2000. [21] Scott C Lester, Glen B Taksler, J Griff Kuremsky, et al. Clinical and economic outcomes of patients with brain metastases based on symptoms: an argument for routine brain screening of those treated with upfront radiosurgery. Cancer, Feb 2014. [22] Silvia Scoccianti, Umberto Ricardi. Treatment of Brain Metastases: Review of Phase III Randomized Controlled Trials. Radiother Oncol., Feb 2012. [23] Sofia Visa, Brian Ramsay, Anca Ralescu, and et al. Confusion Matrix-based Feature Selection. Proceedings of The 22nd Midwest Artificial Intelligence and Cognitive Science Conference 2011, 2011. [24] Stephen V.Stehman. Selecting and Interpreting Measures of Thematic Classification Accuracy. Remote Sensing of Environment, Aug 1997. [25] Streiner DL and Cairney J. What’s under the ROC? An introduction to receiver operating characteristics curves. Can J Psychiatry, Feb 2007. [26] Terry K. Koo and Mae Y. Li. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Chiropractic Medicine, Mar 2016. [27] Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. arXiv.org, Mar 2016. [28] Yan-qi Huang, Chang-hong Liang, Lan He, et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. Journal of Clinical Oncology, Jun 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54117 | - |
| dc.description.abstract | 腫瘤治療預後評估是癌症醫學的基石，放射影像則扮演癌症治療評估的關鍵角色，腦部轉移腫瘤接受單獨立體定位放射手術後，相對於傳統的全腦放射線治療，雖然會有較好的認知功能及生活品質，術後但顱內遠端復發腫瘤的比例較高，且這些個案通常伴隨著較差的預後，過去曾有研究以腦內轉移腫瘤體積、顱內轉移腫瘤顆數、全身腫瘤惡化與否來進行預後預測，但預測效果並沒有良好的泛用性及準確性，因此發展一個精準而穩固的轉移腦部腫瘤遠端顱內復發預測模型是臨床上一個重要的課題。影像體學是基於分析醫學影像的影像體特徵是一個新穎、可行且可擷取巨量特徵的方法，標準化影像體特徵結取方法，包括經由引用深度學習影像擴增方法中的平移、旋轉及脹縮，來篩選得到具穩固性的影像體特徵，並加以結合基於機器學習的非線性的降維演算法，希望可以並提升其泛用性，並保有預後預測效果，本研究目標在建立具泛用性且精確性的影像體學預後預測模型，以期能幫助選擇最適合腦轉移腫瘤的第一線治療方式。本計畫預計分析接受立體定位放射手術治療之腦轉移腫瘤病人之磁振造影影像資料集，嘗試預測腦轉移腫瘤受立體定位放射手術之腫瘤復發預後，並建立影像研究之標準流程。 | zh_TW |
| dc.description.abstract | Early tumor prognosis prediction is the cornerstone of cancer medicine. Radiographic imaging plays an important role to describe and predict the outcome of cancer treatment. Whole-brain radiotherapy (WBRT) possessed the historical essential role for multiple brain metastases (BM) treatment in past decades, whereas emerging evidence by several randomized trials supported stereotactic radiosurgery (SRS) alone, instead of upfront WBRT, to be the preferred treatment modality for limited numbers of BM (defined as 1 to 4). SRS alone provides better cognitive and quality of life (QOL) outcomes compared with WBRT and does not compromise with a detrimental effect on survival. However, time and labor costs to design an SRS plan are substantially higher than WBRT, and increased risk of intracranial progression after SRS alone also requires additional salvage SRS or WBRT. Historically, some studies provided a predictive model incorporating pre-SRS tumor volume, tumor numbers, and systemic cancer progression status to predict the intracranial distant progression, but those models lack accuracy and generalization. Radiomic analysis based on radiographic imaging is a novel and efficient approach to extract extensive image features followed by reducing the dimensionality of the features to robust key components with the assistance of high-throughput computing. In addition, radiomic feature extraction by using image augmentation tips, including translation, rotation, and inflation/retraction, and radiomic features selection by using infraclass variation coefficient will provide a robust way to get radiomic features. This study aims to use a brain Magnetic Resonance Imaging (MRI) dataset derived from the National Taiwan university hospital radiosurgery imaging database to develop a machine-learning-based robust radiomic model to predict the distant intracranial progression of patient of BMs treated with SRS. Furthermore, we also establish a standard workflow to analyze the radiographic imaging biomarker. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:40:42Z (GMT). No. of bitstreams: 1 U0001-0308202023180200.pdf: 3453008 bytes, checksum: 2853b9d9a93fd235d653c8cf38432f75 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Data Description and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1 Brain Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.1 Computed Tomography . . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . . 7 3.2 Brain Metastasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.3 Introduction of Data Preprocessing . . . . . . . . . . . . . . . . . . . . . 8 3.4 Steps in Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.4.1 N4-ITK Bias Field Correction . . . . . . . . . . . . . . . . . . . 9 3.4.2 Resample Image Spacing . . . . . . . . . . . . . . . . . . . . . . 11 3.4.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5 Stochastic Perturbation Simulation . . . . . . . . . . . . . . . . . . . . . 17 3.5.1 Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.5.2 Rotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.5.3 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.5.4 Surface Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1 Extreme Gradient Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.1 Classification And Regression Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.2 Gradient Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.3 Extreme Gradient Boosting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1.4 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 t-Distributed Stochastic Neighbor Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2.1 Stochastic Neighbor Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2.2 t-Distributed Stochastic Neighbor Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.3 Advantages and Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.3 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5 Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1 Radiomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1.1 Definition and Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1.2 Shape and Volume Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.1.3 First-Order Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.1.4 Second-Order Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.5 High-Order Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.6 Robust Features Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 Intraclass Correlation Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.3 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.4 Area Under the Curve of Receiver Operating Characteristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.4.1 Receiver Operating Characteristic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.4.2 Area Under the Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.5 Precision-Recall Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 6 Experiment and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.2.1 Robust radiomic features selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.2.2 Comparison of classification ability between of raw image, pre- processed imaging using conventional modality, and preprocessed imaging using perturbation series based modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.3 Radiomic features derived from raw image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.4 Radiomic features derived from resampled image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2.5 Radiomic features derived from normalized image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2.6 Radiomic features derived from perturbation based radiomic features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2.7 Regularity of radiomic features derived from perturbation based radiomic features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 .1 Methodology of Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 .1.1 Image gray-level normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 .1.2 Image gray-level normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 .1.3 Radiomic Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 | |
| dc.language.iso | en | |
| dc.title | 增進基於核磁共振之影像體學特徵的前處理以提高影像體學預後預測模型之準確度以及泛用性 | zh_TW |
| dc.title | Elaborating the Preprocessing of Radiomics Features Derived from Brain Magnetic Resonance Images to Improve the Precision and Regularity of Radiomic-based Prognosis Prediction Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李宏毅(Hung-yi Lee),廖偉智(Wei-Chih Liao) | |
| dc.subject.keyword | 腦影像,機器學習,影像體學,影像前處理, | zh_TW |
| dc.subject.keyword | brain image,machine learning,Radiomics,image preprocessing, | en |
| dc.relation.page | 77 | |
| dc.identifier.doi | 10.6342/NTU202002329 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-05 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
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