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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙(Hsiu-Hsi Chen) | |
dc.contributor.author | Yi-Ying Wu | en |
dc.contributor.author | 吳怡瑩 | zh_TW |
dc.date.accessioned | 2021-06-16T16:07:28Z | - |
dc.date.available | 2013-09-24 | |
dc.date.copyright | 2013-09-24 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-06-10 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62686 | - |
dc.description.abstract | 研究背景 客製化乳癌篩檢為減少篩檢所造成之傷害與成本,並增加族群篩檢效益的萬靈丹。此問題可透過建立三階段馬可夫模式或考量癌症特徵(如淋巴結侵襲)後之五階段模式後,將族群分層後,在高危險族群施行較密集之篩檢來降低偽陰性率與針對低危險族群降低篩檢頻次達到減低偽陽性率的作法來同時解決。然而先前文獻甚少提及多階段馬可夫模式於客製化乳癌之發展與應用,因此本論文之研究目的不僅包含馬可夫模式之建立並同時將其應用於乳癌篩檢計畫。
材料與方法 透過文獻回顧方式蒐集影響疾病進展到臨床前期之起始因子與進展到臨床期之促進因子之訊息。我們架構一整合現存與最新之遺傳學發現、生物標記與傳統危險因子之三階段乳癌風險評估模式,並利用估計結果建立危險分數將族群分為不同風險族群,依不同風險群決定不同之起始篩檢年齡與篩檢間隔與替代篩檢工具來建立客製化篩檢計畫。 隨後拓展三階段模式至考量淋巴侵襲之五階段模式中,然而此時文獻資訊對建立五階段模式並不充份,因此我們提出再參數化的估計方法來估計不同狀態之參數。並進一步應用瑞典Dalarna郡自1977到2010的篩檢資料來架構風險模式。 結果 我們自文獻萃取出之起始因子包含BRCA基因、7個單核甘酸多型性、乳房密度、身體質量指數與初孕年齡;促進因子包含身體質量指數、初孕年齡、ER、HER-2與Ki-67之表現。依據百萬台灣婦女之模擬結果,共2182為盛行篩檢偵測個案、2167為後續篩檢偵測個案與1560為篩檢間隔間個案。在帶有BRCA突變基因個案區分為高風險族群(75百分位) 、中風險族群(50百分位)與低風險族群(25百分位)之10年自無乳癌狀態轉移至臨床期之風險分別為25.83%、20.31% 與13.84%. 其對應在無帶有BRCA基因突變之個案風險在高、中、低風險群分別為1.55%、1.22% 與 0.76%。根據論文所建立之以風險分數為基礎之客製化篩檢相較於全面採用3年篩檢間隔之策略可有效減低30%篩檢間隔個案發生率比上期望乳癌發生率,並減少8.2%的偽陽性率。 就五階段馬可夫模式而言,採用我們所提出之估計方法所得之估計結果與真值或採用完整資料下利用最大概似估計法所得之結果相似,模擬結果證實利用表列資訊合併外部訊息來估計促進因子之效果是為有效的。 此外,根據瑞典實證料結果顯示,身體質量指數、初孕年齡、乳房密度與家族病史為起始因子,而身體質量指數、乳房密度與乳癌分子表現型為促進因子。 結論 我們提出多階段風險評估馬可夫模式用以模式化一群起始因子與促進因子影響之乳癌進展過程。並架構危險分數應用於客製化篩檢計畫中。藉由調整危險分數與其各自之遺傳因子、腫瘤特徵、臨床因子與其他危險因子後,此概念與方法將可應用於不同族群的其他篩檢計畫。 | zh_TW |
dc.description.abstract | Background Individually tailored screening for breast cancer is now a panacea for reducing the concern expressed by health policy makers that the harm and cost of screening should be minimized and the benefits maximized. The two-throng problem may be solved by intensive screening on high-risk groups to reduce the false-negative cases and reducing the frequency of screen on low-risk groups to reduce the false-positive cases through the risk stratification done by the superimposition of initiators and promoters using the three-state Markov model and also the five-state Markov model, if possible, by incorporating tumor attributes. The application of the multistate Markov model to facilitate the development of individually tailored screening has been hardly addressed. The subjects of this thesis not only embrace the development of the Markov model but also demonstrate how these proposed models can be applied to individually tailored breast cancer screening.
Materials and Methods The thesis begins with literature search for initiators and prompters related to the development of breast cancer in the preclinical detectable phase (PCDP) and the clinical phase (CP), respectively. We proposed a risk-score-based approach that translates state-of-the-art scientific evidence, including genomic discovery, biomarkers, and conventional risk factors, into the initiators and promoters underpinning a novel multi-factorial three-state temporal natural history model. The estimated results are used to construct the risk scores to stratify population into different risk groups and to assess the optimal age to begin screening and the inter-screening interval for each category, and to ascertain which high risk group requires an alternative imagine technique. We extended the three-state model to five-state model with the incorporation of node involvement, however the information from literature is insufficient. We propose an estimation method based on the reparameterization method to estimate the state-specific parameters. Furthermore, the empirical screening data from 1977 to 2010 in Dalarna county, Sweden was used to construct the risk assessment model. Results From the literature, we identify the initiators including BRCA gene, 7 Single nucleotides polymorphisms (SNPs), breast density, body mass index (BMI), and age at first full-term pregnancy (AP) and the promoters including BMI, AP, ER, HER-2 and Ki-67 expression. According to the simulated results based on one-million Taiwanese women, there were 2182, 2867 and 1560 prevalent screen-detected (SD) cases, incident SD cases and interval cancers. The 10-year predicted risk for the transition from Free of breast cancer (FBC) to the clinical phase (CP) was 25.83% for high risk group (75th percentile), 20.31% for intermediate risk group (50th percentile), and 13.84% for low risk group (25% percentile), respectively in BRCA-carrier. The corresponding figures were 1.55% for high risk group, 1.22% for intermediate risk group, and 0.76% for low risk group in non-carrier. This risk-score-based approach significantly reduced the interval caner rate as a percentage of expected rate in the absence of screening by 30% compared with triennial screenings, and it also reduced the false-positive cases by 8.2%. For the five-state Markov model, the estimated results of the proposed method were similar to the maximum likelihood estimates (MLE) based on full data analysis and also the true values. The simulated results show the proposed method which only used the aggregate data rather than individual data combined with the external information is valid for estimate the effects of promoters. Based on the Sweden empirical data, we identified the initiators including BMI, AP, breast density, and family history and promoters including BMI, breast density, and molecular phenotypes. Conclusions We proposed risk assessment Markov models for modeling the progression of breast cancer as a function of a constellation of initiators and promoters, and used the estimated results to construct composite risk scores for individually tailored screening. The concept and approach can be readily applied to other screening programs in other populations by tuning risk scores with their own genetic susceptibility factors, tumor phenotypes, clinical attributes, and risk factors. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T16:07:28Z (GMT). No. of bitstreams: 1 ntu-102-D96842010-1.pdf: 7066599 bytes, checksum: af7ed5548ef822fa93c9525faf4f184b (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iv CONTENTS viii LIST OF FIGURES xii LIST OF TABLES xiii Chapter 1 Introduction 1 1.1 Individually Tailored Breast Cancer Screening 1 1.2 Superimposition of State-specific Covariates into Temporal Natural History of Breast Cancer 2 1.3 Multistate Markov Process for Temporal Natural History of Breast Cancer 4 1.4 Aims 7 Chapter 2 Literature Review 10 2.1 Literature Review of Initiators and Promoters Associated with Three-state Progression of Breast Cancer 10 2.1.1 Initiators 10 2.1.2 Promoters 13 2.2 Literature Review of the Multistate Models 14 Chapter 3 Data Sources 33 3.1 Simulated Taiwanese One Million Data with Certain Covariates Obtained from Literature Review 34 3.2 The Taiwanese Multicenter Cancer Screening (TAMCAS) Dataset 36 3.3 The Sweden Empirical Data 37 3.3.1 Study Subjects 37 3.3.2 Study Design 39 3.3.3 Covariates 40 Chapter 4 Model Specification 42 4.1 Three-state Non-homogeneous Markov Model 42 4.1.1 The Incorporation of State-specific Covariates 43 4.2 Five-state Model 44 4.2.1 The Incorporation of State-specific Covariates 45 4.2.2 Reparameterization 47 4.3 Data Simulation 50 4.3.1 Three-state Model for One-million Taiwanese Screening Cohort 50 4.3.2 Five-state Model 54 4.4 Likelihood Function 59 4.4.1 Three-state Model for One-million Taiwanese Screening Cohort 59 4.4.2 Five-state Model for Simulated Cohort 61 4.4.3 Three-state Model for Sweden Empirical Data 63 4.5 Model Validation 65 4.5.1 Three-state Model for One-million Taiwanese Screening Cohort 65 4.5.2 Five-state Model for Proposed Method 67 4.6 Applications 67 4.6.1 Individually Tailored Screening for Taiwanese One-million Cohort 67 4.6.2 Five-state Model for Proposed Method 69 4.6.3 Three-state Model for Sweden Empirical Data 69 Chapter 5 Results 70 5.1 Taiwanese One-million Screening Cohort 70 5.1.1 Descriptive Findings 70 5.1.2 Estimated Results Based on Three-state Model 71 5.1.3 Composite Score for Multistate Risk Prediction 71 5.1.4 Predicted Cumulative Risk of Breast Cancer (Commencing from FBC) by Risk Groups 73 5.1.5 Promoter-specific Mean Sojourn Times 74 5.2 Application to Individually Tailored Screening Program 75 5.3 Estimated Results of the Proposed Method for Five-state Model 77 5.3.1 Estimated Results of Simulated Data 77 5.3.2 Application for the Five-state Model 78 5.3.3 Estimated Results of Sweden Empirical Data 79 5.4 Three-state Model Results of Sweden Empirical Data 80 5.5 Validation Results 84 5.5.1 Three-state Model for One-million Taiwanese Cohort 84 5.5.2 Five-state Model for Proposed Method 84 Chapter 6 Discussion 86 6.1 Application to Individually Tailored Screening 86 6.2 Risk Classification of Taiwanese Screening Cohort 92 6.3 Results from the Sweden Empirical Data 96 6.4 Merits of Using Markov Regression Model 99 6.5 Conclusions 103 TABLES 104 FIGURES 125 REFERENCE 137 | |
dc.language.iso | en | |
dc.title | 多階段馬可夫模式於客製化乳癌篩檢之應用 | zh_TW |
dc.title | Multistate Markov Model for Individually Tailored Breast Cancer Screening | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 張金堅,林明薇,陳保中,丘政民,于承平 | |
dc.subject.keyword | 多階段馬可夫模式,客製化篩檢,乳癌, | zh_TW |
dc.subject.keyword | Multistate Markov Model,Individually Tailored Screening,Breast Cancer, | en |
dc.relation.page | 142 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-06-10 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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