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Abstract A28: Risk model for clinical management of HPV-infected women 摘要A28: hpv感染妇女临床管理的风险模型
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-A28
M. Demarco, Noorie Hyun, H. Katki, B. Befano, L. Cheung, Tina Raine-Bennett, B. Fetterman, T. Lorey, N. Poitras, J. Gage, P. Castle, N. Wentzensen, M. Schiffman
Background: The natural history of human papillomavirus (HPV) and the steps leading to cervical cancer are well-known; the steps include infection with one of the 13 carcinogenic HPV genotypes, viral persistence, progression to precancer, and invasion. Cervical screening programs target treatable cervical precancer to prevent cancer mortality and morbidity. HPV infections are very common and only those causing precancer pose a risk of cancer. In addition to HPV genotype, multiple established co-factors can be combined to predict with unparalleled accuracy and precision the broad range of risks for the critical transition from common HPV infection to uncommon cervical precancer. Thus, there are three types of factors predicting risk of precancer: viral (e.g., HPV genotype and viral load), host (e.g., age, race/ethnicity) and behavioral (e.g., oral contraceptive use, smoking, BMI, co-infection with other sexually transmitted agents). We are building a risk prediction model for clinical use that reflects the determinants of HPV natural history. The absolute-risk based model will consider the three possible HPV outcomes: HPV progression, else HPV “clearance” (immune suppression) signifying low risk of subsequent precancer from that infection, else persistence of HPV infection without either progression or clearance (i.e., still unresolved outcome). To estimate these competing risks for all the factors, cofactors and their combinations requires very large cohorts of HPV-infected women. Methods: Our analysis makes use of data from a uniquely large cohort study of HPV-infected women, specifically, the 35,000 HPV-positive women, 30 years or older, from the NCI-Kaiser Permanente Northern California Persistence and Progression cohort study. The median time of follow-up is 3 years (maximum >7 years). Risk predictors already recorded include: woman9s age, HPV infection status, HPV genotype, viral load, concurrent cervical cytology result, and the range of behavioral cofactors. We will present at the meeting the steps leading to the final model: 1) univariate, then multivariate, absolute risks of progression, clearance, or persistence for each HPV genotype; 2) the same risks accounting for time to event and loss-to-followup; and 3) the novel statistic mean risk stratification (MRS), which measures how well the model predicts the crucial dichotomous outcome (progression vs. not). MRS identifies which combination of variables, by virtue of frequency of positive results and strength of risk stratification, is most promising in deciding risk-based clinical management (i.e., who needs colposcopic biopsy due to high risk of precancer). We present the univariate absolute risks for HPV genotypes here, but will show the full multivariate proportional hazards and MRS analyses at the conference. Results: Risk of progression (29.4% for HPV16 to 7.2% for HPV68) varied inversely with risk of clearance (60.1% for HPV16 to 81.6% for HPV68), by HPV type. Relatively few (~10%)
背景:人类乳头瘤病毒(HPV)的自然历史和导致宫颈癌的步骤是众所周知的;步骤包括感染13种致癌HPV基因型之一,病毒持续存在,进展到癌前病变和侵袭。子宫颈筛查计划的目标是可治疗的宫颈癌前病变,以预防癌症的死亡率和发病率。HPV感染很常见,只有那些引起癌前病变的人才有患癌症的风险。除了HPV基因型外,多种已确定的辅助因素可以结合起来以无与伦比的准确性和精度预测从常见HPV感染到罕见宫颈癌前病变的关键转变的广泛风险。因此,有三种预测癌前风险的因素:病毒(例如,HPV基因型和病毒载量)、宿主(例如,年龄、种族/民族)和行为(例如,口服避孕药的使用、吸烟、体重指数、与其他性传播媒介的合并感染)。我们正在建立一个临床使用的风险预测模型,反映HPV自然史的决定因素。基于绝对风险的模型将考虑三种可能的HPV结果:HPV进展,否则HPV“清除”(免疫抑制)意味着该感染的后续癌前病变风险较低,否则HPV感染持续存在,既没有进展也没有清除(即仍未解决的结果)。为了估计所有因素、辅助因素及其组合的竞争风险,需要非常大的hpv感染妇女队列。方法:我们的分析利用了来自一项独特的大型hpv感染妇女队列研究的数据,特别是来自NCI-Kaiser Permanente北加州持续和进展队列研究的35000名30岁或以上的hpv阳性妇女。中位随访时间为3年,最长随访时间为7年。已经记录的风险预测因素包括:女性年龄、HPV感染状况、HPV基因型、病毒载量、并发宫颈细胞学结果以及行为辅助因素的范围。我们将在会议上介绍最终模型的步骤:1)单因素,然后是多因素,每个HPV基因型的进展,清除或持续的绝对风险;2)对事件发生时间和事后损失进行相同的风险核算;3)新的统计平均风险分层(MRS),它衡量模型预测关键的二分类结果(进展与不进展)的程度。MRS通过阳性结果的频率和风险分层的强度来确定哪些变量组合在决定基于风险的临床管理(即,由于癌前病变的高风险,谁需要阴道镜活检)方面最有希望。我们在这里展示了HPV基因型的单因素绝对风险,但将在会议上展示完整的多因素比例风险和MRS分析。结果:不同HPV类型的进展风险(HPV16为29.4%,HPV68为7.2%)与清除率(HPV16为60.1%,HPV68为81.6%)呈负相关。相对很少(约10%)的致癌性感染没有进展。在初步分析中,最重要的单变量辅助因素是病毒载量(主要是HPV16)、女性年龄和并发细胞学。没有特别重要的行为风险因素。清除时间和进展时间不因HPV类型而异,发生事件的中位时间为1.5-2年。结论:根据我们的初步结果,大多数HPV感染的命运是在首次检测后的几年内确定的,主要基于病毒的特征。MRS总结了预测模型的平均风险判别与试验前概率的比较,从而可以估计其预期收益。我们假设并将检验绝对风险的多变量计算和平均风险分层的使用是否可以改善hpv感染妇女的基于风险的临床管理。引文格式:Maria Demarco, Noorie Hyun, Hormuzd Katki, Brian Befano, Li b张,Tina R. Raine-Bennett, Barbara Fetterman, Thomas Lorey, Nancy Poitras, Julia C. Gage, Phillip E. Castle, Nicolas Wentzensen, Mark Schiffman。hpv感染妇女临床管理的风险模型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr A28。
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引用次数: 0
Abstract A15: Breast cancer subtype subsequent to a benign breast biopsy among African American women 摘要A15:非裔美国妇女良性乳腺活检后的乳腺癌亚型
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-A15
J. Ruterbusch, M. Cote, J. Boerner, E. Abdulfatah, B. Alosh, V. Pardeshi, M. F. Daaboul, Woodlyne Roquiz, R. Ali-Fehmi, S. Bandyopadhyay
Introduction: Most clinical models to estimate risk of invasive breast cancer include history of benign breast disease (BBD) as a covariate, as these women represent a higher risk group compared to the general population. A better understanding of the association between BBD and breast cancer is necessary to improve the utility of these risk models, particularly with respect to tumor subtype. This may be especially important for African American women who are more likely to present with aggressive cancers compared to white women. Here we present tumor subtypes from a higher risk cohort of African American women with a history of BBD. Methods: Benign breast biopsies from 3,865 African American women with BBD diagnosed from 1997-2010 were examined for 14 benign features, and followed for subsequent breast cancers in metropolitan Detroit, Michigan using medical records and data from the Detroit Surveillance, Epidemiology and End Results (SEER) program. Immunohistochemistry analysis was performed for the following 6 markers: estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki-67, epidermal growth factor receptor (EGFR) and cytokeratin 5/6 (CK 5/6) in order to categorize the subsequent breast cancers by subtype. Briefly, ER and PR were utilized to classify tumors as luminal or non-luminal, and then further classification was made based HER2. Luminal tumors were also classified by Ki-67 expression, and triple negative tumors (ER/PR/HER2 negative) were further classified based on expression of either CK5/6 or EGFR, resulting in 6 categories. Results: 210 women (5.4% of the total cohort) with a subsequent breast cancer were identified over a median follow-up time of 12.3 years (range: 0.6 - 18.0). Analysis of all 6 markers is complete for half of the tumors (104). The majority of the subsequent cancers were invasive (n=72, 69.2%). Most of the invasive tumors were luminal B, HER2- (37.5%), followed by luminal A (31.9%), triple negative (19.4%), non-luminal, HER2+ (6.9%) and luminal B, HER2+ (4.2%). Of the 14 triple negative cancers (19.4%), 8 were negative for CK5/6 and EGFR (5 negative phenotype, 57.1%) and 6 were core basal (42.9%). Among the 32 in situ tumors, the majority were luminal A (n=26, 81.3%), followed by luminal B, HER2- (n=5, 15.6%) and there was a single tumor classified as 5 negative. Compared to population-based SEER data from 5,268 African American women with invasive breast cancer and available data on 3 markers (ER, PR, and HER2) diagnosed in 2010, our cohort is similar with respect to tumor subtype. Conclusions: The women with a previous benign breast biopsy in our cohort who develop a subsequent breast cancer have subtypes that are similar to the general African American population in the United States. Thus, our BBD cohort represents the full spectrum of invasive breast cancers with respect to subtype, including triple negative tumors. Citation Format: Julie J. Ruterbusch, Michele
大多数评估浸润性乳腺癌风险的临床模型都将乳腺良性疾病史(BBD)作为协变量,因为与一般人群相比,这些女性代表着更高的风险群体。更好地了解BBD和乳腺癌之间的关系对于提高这些风险模型的效用是必要的,特别是在肿瘤亚型方面。这对非裔美国女性尤其重要,因为她们比白人女性更容易患上侵袭性癌症。在这里,我们介绍了具有BBD病史的高风险非裔美国妇女的肿瘤亚型。方法:利用底特律监测、流行病学和最终结果(SEER)项目的医疗记录和数据,对1997-2010年诊断为BBD的3865名非裔美国妇女进行乳腺良性活检,检查14个良性特征,并随访密歇根州底特律大都会的后续乳腺癌。通过免疫组化分析雌激素受体(ER)、孕激素受体(PR)、人表皮生长因子受体2 (HER2)、Ki-67、表皮生长因子受体(EGFR)和细胞角蛋白5/6 (CK 5/6) 6项标志物,对随后发生的乳腺癌进行亚型分类。简单地用ER和PR将肿瘤分为管腔和非管腔,再根据HER2进一步分类。根据Ki-67的表达对腔内肿瘤进行分类,并根据CK5/6或EGFR的表达进一步对三阴性肿瘤(ER/PR/HER2阴性)进行分类,共分为6类。结果:210名女性(占总队列的5.4%)在12.3年(范围:0.6 - 18.0)的中位随访时间中被确定为乳腺癌。半数肿瘤(104)的6种标记物分析完全。大多数后续癌症为侵袭性(n=72, 69.2%)。浸润性肿瘤以腔内B、HER2-居多(37.5%),其次为腔内A(31.9%)、三阴性(19.4%)、非腔内HER2+(6.9%)和腔内B、HER2+(4.2%)。在14例三阴性癌症中(19.4%),8例CK5/6和EGFR阴性(5例表型阴性,57.1%),6例核心基础(42.9%)。32例原位肿瘤中,以管腔A为主(n=26,占81.3%),其次为管腔B、HER2- (n=5,占15.6%),5阴性1例。与5268名非裔美国浸润性乳腺癌妇女基于人群的SEER数据和2010年诊断的3种标志物(ER, PR和HER2)的可用数据相比,我们的队列在肿瘤亚型方面相似。结论:在我们的队列中,先前进行良性乳腺活检的妇女随后发展为乳腺癌,其亚型与美国一般非裔美国人相似。因此,我们的BBD队列代表了浸润性乳腺癌的所有亚型,包括三阴性肿瘤。引文格式:Julie J. Ruterbusch, Michele L. Cote, Julie Boerner, Eman Abdulfatah, Baraa Alosh, Vishakha Pardeshi, MHD Fayez Daaboul, Woodlyne Roquiz, Rouba Ali-Fehmi, Sudeshna Bandyopadhyay。非裔美国妇女良性乳腺活检后的乳腺癌亚型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr A15。
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引用次数: 0
Abstract B01: Testing a multilevel risk prediction model in high risk men enrolled in a prostate cancer early detection program 摘要/ Abstract摘要:在前列腺癌早期检测项目的高危男性中,测试多水平风险预测模型
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-B01
Shannon M. Lynch, E. Handorf, Elizabeth Blackman, L. Bealin, S. Daniel, V. Giri, E. Obeid, C. Ragin, M. Daly
Background: Health disparities play a major role in prostate cancer (PCa). African American (AA) compared to European American (EA) men are twice as likely to die of and be diagnosed with PCa. Multilevel factors from societal/neighborhood exposures down to genetics likely contribute to racial disparities, but few PCa risk prediction models include multilevel factors and consider race/ethnic differences. Objective: We sought to: 1) develop a multilevel risk prediction model for time to PCa diagnosis, that includes neighborhood variables, individual-level socioeconomic and clinical factors (education, race, digital rectal exam or DRE), and biologic variables (prostate specific antigen or PSA level, and percent West African genetic ancestry) in men at high risk for prostate cancer (defined as AA men and/or men with a PCa family history); 2) compare our multilevel model to a more standard prediction model that includes only age, race, PSA, and DRE (abnormal/normal). Methods: A total of 443 high risk, cancer-free men between 35 and 69 years of age with complete socioeconomic, racial, and genetic ancestry data were identified from the Prostate Risk Assessment Program (PRAP) at Fox Chase Cancer Center. Their data were geocoded and linked to 17 neighborhood variables at the census tract level (from the Year 2000 U.S. Census) that were previously associated with advanced PCa in EA men in a novel neighborhood-wide association study(NWAS) our study team developed. These variables generally represent neighborhood transportation, poverty, income, social support, immigration, renting/owning a house, and employment. Men were followed from time of program (PRAP) entry to PCa diagnosis or censoring, with annual follow-up visits that included PSA and DRE screening. Men with elevated PSA or other indications for PCa were referred to Urology for evaluation and potential biopsy according to PRAP protocols. Univariate analyses of neighborhood variables, and the interaction of each variable with PSA and race, were evaluated in Cox regression models, using robust standard errors to adjust for clustering by census tract, in order to inform the final multivariate, multilevel model. Harrell9s C Index (C Statistic) was used to compare the multilevel risk prediction model to a standard prediction model. Results: With a median follow-up time of 71 months, PCa diagnosis occurred in 69 participants. The final multilevel risk prediction model included 3 neighborhood variables related to transportation, social support, and poverty, along with education, age, race, baseline PSA, baseline DRE, and PCa family history. Significant interactions between the top hit from the NWAS and PSA were noted in the full study population (neighborhood mode of transportation to work X PSA, p-value Conclusion: This study is the first to investigate the role of neighborhood in PCa risk prediction. While risk prediction models show little change, significant neighborhood effects in multilevel models w
背景:健康差异在前列腺癌(PCa)中起着重要作用。非裔美国人(AA)与欧洲裔美国人(EA)相比,死于前列腺癌和被诊断为前列腺癌的可能性是欧洲裔美国人的两倍。从社会/社区暴露到遗传的多水平因素可能导致种族差异,但很少有PCa风险预测模型包括多水平因素并考虑种族/民族差异。目的:我们试图:1)建立一个前列腺癌诊断时间的多水平风险预测模型,该模型包括邻居变量,个人水平的社会经济和临床因素(教育,种族,直肠指检或DRE),以及前列腺癌高危男性(定义为AA男性和/或有前列腺癌家族史的男性)的生物学变量(前列腺特异性抗原或PSA水平,西非遗传血统百分比);2)将我们的多层模型与仅包括年龄、种族、PSA和DRE(异常/正常)的更标准的预测模型进行比较。方法:从Fox Chase癌症中心的前列腺风险评估项目(PRAP)中确定了443名年龄在35至69岁之间的无癌高风险男性,他们具有完整的社会经济、种族和遗传血统数据。他们的数据是地理编码的,并与人口普查区水平的17个社区变量(来自2000年美国人口普查)相关联,这些变量先前在我们的研究小组开发的一项新的社区范围关联研究(NWAS)中与EA男性的高级PCa相关。这些变量通常代表社区交通、贫困、收入、社会支持、移民、租房/买房和就业。男性从项目开始(PRAP)到PCa诊断或检查,每年随访包括PSA和DRE筛查。PSA升高或其他前列腺癌指征的男性被转介到泌尿科进行评估,并根据PRAP方案进行活检。邻域变量的单变量分析,以及每个变量与PSA和种族的相互作用,在Cox回归模型中进行评估,使用稳健的标准误差来调整人口普查区的聚类,以便为最终的多变量多层次模型提供信息。采用Harrell9s C指数(C统计量)对多层风险预测模型与标准预测模型进行比较。结果:中位随访时间为71个月,69名参与者被诊断为前列腺癌。最终的多层次风险预测模型包括与交通、社会支持和贫困相关的3个邻里变量,以及教育、年龄、种族、基线PSA、基线DRE和PCa家族史。在整个研究人群中,NWAS和PSA之间存在显著的相互作用(社区上班交通方式X PSA, p值)。结论:本研究首次探讨了社区在PCa风险预测中的作用。虽然风险预测模型显示变化不大,但多层次模型中的显著邻里效应值得进一步研究,并可能为未来的健康差异研究提供信息。引文格式:Shannon M. Lynch, Elizabeth Handorf, Elizabeth Blackman, Lisa Bealin, Shiju Daniel, Veda N. Giri, Elias Obeid, Camille Ragin, Mary B. Daly。在参加前列腺癌早期检测项目的高风险男性中测试多层次风险预测模型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr B01。
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引用次数: 0
Abstract IA02: A brief overview of building and validating absolute risk models 摘要IA02:建立和验证绝对风险模型的简要概述
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-IA02
R. Pfeiffer
Statistical models that predict disease incidence, disease recurrence or mortality following disease onset have broad public health and clinical applications. Of great importance are models that predict absolute risk, namely the probability of a particular outcome, e.g. breast cancer, in the presence of competing causes of mortality. Although relative risks are useful for assessing the strength of risk factors, they are not nearly as useful as absolute risks for making clinical decisions or establishing policies for disease prevention. That is because such decisions or policies often weigh the favorable effects of an intervention on the disease of interest against the unfavorable effects that the intervention might have on other health outcomes. The common currency for such decisions is the (possibly weighted) absolute risk for each of the health outcomes in the presence and absence of intervention. First, I discuss various approaches to building absolute risk models from various data sources and illustrate them with absolute risk models for breast cancer and thyroid cancer. Before a risk prediction model can be recommended for clinical or public health applications, one needs to assess how good the predictions are. I will give an overview over various criteria for assessing the performance of a risk model. I assume that we have developed a risk model on training data and assess the performance of the model on independent test or validation data. This approach, termed external validation, provides a more rigorous assessment of the model than testing the model on the training data (internal validation); even though cross-validation techniques are available to reduce the over-optimism bias that can result from testing the model on the training data. I present general criteria for model assessment, such as calibration, predictive accuracy and classification accuracy, and discriminatory accuracy. Calibration measures how well the numbers of events predicted by a model agree with the observed events that arise in a cohort. Calibration is the most important general criterion, because if a model is not well calibrated, other criteria, such as discrimination, can be misleading. Discriminatory accuracy measures how well separated the distributions of risk are for cases and non-cases. Another approach is to tailor the criterion to the particular application. I will also present novel criteria for screening applications or high risk interventions. If losses can be specified in a well-defined decision problem, I will show how models can be assessed with respect to how much they reduce expected loss. Citation Format: Ruth Pfeiffer. A brief overview of building and validating absolute risk models. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA02.
预测疾病发病率、疾病复发或疾病发病后死亡率的统计模型具有广泛的公共卫生和临床应用。非常重要的是预测绝对风险的模型,即在存在相互竞争的死亡原因的情况下出现特定结果(例如乳腺癌)的概率。尽管相对风险对评估风险因素的强度有用,但在做出临床决定或制定疾病预防政策方面,它们远不如绝对风险有用。这是因为这样的决定或政策经常权衡干预对相关疾病的有利影响和干预对其他健康结果可能产生的不利影响。这类决策的共同依据是(可能加权的)干预存在和不存在情况下每种健康结果的绝对风险。首先,我讨论了从各种数据源建立绝对风险模型的各种方法,并举例说明了乳腺癌和甲状腺癌的绝对风险模型。在将风险预测模型推荐用于临床或公共卫生应用之前,需要评估预测的准确性。我将概述评估风险模型性能的各种标准。我假设我们已经在训练数据上开发了一个风险模型,并在独立的测试或验证数据上评估模型的性能。这种方法被称为外部验证,它提供了比在训练数据上测试模型(内部验证)更严格的模型评估;尽管交叉验证技术可以减少由于在训练数据上测试模型而导致的过度乐观偏见。我提出了模型评估的一般标准,如校准,预测精度和分类精度,以及区分精度。校准测量的是由模型预测的事件数量与在队列中出现的观察到的事件的一致程度。校准是最重要的一般标准,因为如果一个模型没有很好地校准,其他标准,如歧视,可能会产生误导。歧视性准确性衡量的是案例和非案例之间风险分布的分离程度。另一种方法是为特定的应用程序定制标准。我还将提出筛选应用或高风险干预措施的新标准。如果损失可以在定义良好的决策问题中指定,那么我将展示如何根据模型减少预期损失的程度来评估模型。引文格式:Ruth Pfeiffer。构建和验证绝对风险模型的简要概述。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;癌症流行病学与生物标志物[j]; 2017;26(5增刊):摘要nr IA02。
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引用次数: 0
Abstract IA21: Translating evidence to action: Yourdiseaserisk.wustl.edu IA21:将证据转化为行动:Yourdiseaserisk.wustl.edu
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-IA21
G. Colditz
Not very long ago scientific publication was viewed as the primary dissemination goal of scientific discovery. This viewpoint, however, has evolved substantially over the past 10 - 20 years. While scientific discovery and publication remain key to dissemination of findings, it is now often viewed as a single stage in the spectrum from discovery to the application of research results. The view of effective dissemination must now also include the practical world of policy makers, clinicians, health care organizations, and the public – groups that need good data and good tools to make informed decisions that drive individual, national, and global health. The development of health risk assessment and prevention tools can play a key role in doing this. And such development moves through three general translation stages – with each subsequent stage marked by greater difficulty to achieve. 1) Creation of accurate risk prediction calculation(s) from current evidence base. 2) Development of a practical, usable tool that incorporates the calculation(s) and provides actionable messages – for clinical, public policy, or public use.  3) Integration of risk prediction tools with the social, structural, and financial support for translating recommended action messages into actual action – whether we9re talking about doctors counseling patients, government representatives making health policy, or the public working to improve their own health. This process by necessity requires a multi-disciplinary approach – drawing on expertise from epidemiology, biostatistics, communication theory, coding, and design – among others. With the addition of precision medicine and big data to long-established data analysis techniques, the field of risk prediction is set to expand in coming years. Along with that expansion, it is important to assure that our efforts are valid, useful, reliable, and applicable. Citation Format: Graham A. Colditz. Translating evidence to action: Yourdiseaserisk.wustl.edu. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA21.
不久以前,科学出版物被视为科学发现的主要传播目标。然而,这种观点在过去的10 - 20年里发生了很大的变化。虽然科学发现和发表仍然是传播发现的关键,但现在往往被视为从发现到研究成果应用这一过程中的一个阶段。有效传播的观点现在还必须包括决策者、临床医生、卫生保健组织和公众的实际世界,这些群体需要良好的数据和良好的工具来做出明智的决定,推动个人、国家和全球健康。制定健康风险评估和预防工具可在这方面发挥关键作用。这种发展经历了三个一般的翻译阶段,每个阶段的难度都更大。1)根据现有证据基础建立准确的风险预测计算。2)开发一种实用的、可用的工具,该工具包含计算并提供可操作的信息-供临床、公共政策或公众使用。3)将风险预测工具与社会、结构和财政支持相结合,将建议的行动信息转化为实际行动——无论是医生为患者提供咨询,政府代表制定卫生政策,还是公众努力改善自己的健康。这一过程必然需要一种多学科的方法——利用流行病学、生物统计学、传播理论、编码和设计等方面的专业知识。随着精准医疗和大数据的加入,长期建立的数据分析技术,风险预测领域将在未来几年扩大。随着这种扩大,重要的是要确保我们的努力是有效的、有用的、可靠的和适用的。引文格式:Graham A. Colditz。将证据转化为行动:Yourdiseaserisk.wustl.edu。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要/ Abstract
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引用次数: 0
Abstract IA17: Risk prediction modeling in lung cancer: How can we improve? 摘要:肺癌风险预测建模:如何改进?
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-IA17
J. Field, M. Marcus
Screening for lung cancer The results of the US National Lung Screening Trial (NLST) were published in 2011 and are considered a landmark event in lung cancer research. This randomised study of 53,454 individuals showed that computed tomography (CT) scans are able to reduce lung cancer mortality by 20% through early detection, although with important cost and morbidity due to overdiagnosis and treatment of benign nodule. A number of European pilot trials have reported, we await the NELSON, which is the only statistically powered screening trial in Europe. There are now discussions on how to implement lung cancer screening throughout the world, within differing health care systems. The success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. Risk prediction models Thus accurate selection of high-risk individuals for lung cancer screening requires robust methods for risk prediction. The discriminative performance of a risk model depends not only on the identification of individual risk factors, but also on the influence of these risk variables in the presence/absence of other variables, how accurately these factors can be measured, and the appropriateness of the population and statistical techniques used for modeling. However, the main practical application of a risk prediction model is its use by non-specialists for selection of suitable high-risk people for lung cancer screening/intervention. In addition, to being technically detailed and accurate, a risk model needs to be sufficiently user-friendly to be applied in the general population and/or primary care setting. In practical terms, this means that the risk variables should be straightforward to elicit, and the algorithm should be simple to run. Current lung cancer prediction models The Lung cancer risk prediction models which have been developed include Bach, Spitz, LLP and more recently the PLCO [1] and EPIC model. The UK Lung cancer Screening trial (UKLS) [2] has been the only RCT trial to date, to select high risk individuals from a population based study for a screening trial, utilising a validated risk prediction model [3]. The data already analysed from the UKLS population based approach will provide valuable information as to how to we should implement lung cancer screening, if it becomes a national programme. Utilisation of LLPv2 risk model on UKLS screening trial The LLPv2 risk model has been used to select high-risk individual in the UKLS. UKLS is a randomised controlled trial of LDCT for lung cancer screening, following the Wald single-screen design. In short, the UKLS randomised subjects based on their ≥5% risk of developing lung cancer in the next five years. Using this selection criterion shows that screening programme can potentially be more cost-effective if it is limited to the high-risk segment of the population [2]. Risk models to evaluate indeterminate nodules [4,
美国国家肺筛查试验(NLST)的结果于2011年发表,被认为是肺癌研究的一个里程碑事件。这项涉及53,454人的随机研究表明,通过早期发现,计算机断层扫描(CT)能够将肺癌死亡率降低20%,尽管由于良性结节的过度诊断和治疗,其成本和发病率很高。一些欧洲试点试验已经报告,我们等待NELSON,这是欧洲唯一的统计支持筛选试验。目前正在讨论如何在世界各地不同的卫生保健系统内实施肺癌筛查。肺癌筛查的成功将取决于确定具有足够风险的人群,以便最大限度地提高干预措施的利害比。因此,准确选择肺癌筛查的高危人群需要可靠的风险预测方法。风险模型的判别性能不仅取决于对个别风险因素的识别,还取决于这些风险变量在存在/不存在其他变量的情况下的影响,这些因素的测量准确度,以及用于建模的人口和统计技术的适当性。然而,风险预测模型的主要实际应用是由非专业人员使用它来选择合适的高风险人群进行肺癌筛查/干预。此外,除了在技术上详细和准确外,风险模型还需要足够方便用户,以便在一般人群和/或初级保健环境中应用。在实践中,这意味着风险变量应该是直接引出的,并且算法应该易于运行。目前的肺癌预测模型已开发的肺癌风险预测模型包括Bach, Spitz, LLP和最近的PLCO[1]和EPIC模型。英国肺癌筛查试验(UKLS)[2]是迄今为止唯一的随机对照试验,从基于人群的研究中选择高风险个体进行筛查试验,利用经过验证的风险预测模型[3]。已经从UKLS基于人口的方法中分析的数据将为我们应该如何实施肺癌筛查提供有价值的信息,如果它成为一个国家项目。LLPv2风险模型在UKLS筛查试验中的应用LLPv2风险模型已被用于UKLS高风险个体的筛选。UKLS是一项LDCT用于肺癌筛查的随机对照试验,遵循Wald单筛设计。简而言之,UKLS根据受试者在未来5年内患肺癌的风险≥5%进行随机分组。使用这一选择标准表明,如果筛查计划仅限于高危人群,那么它可能更具成本效益。评估不确定结节的风险模型[4,5]肺癌CT筛查的基础是识别处于可疑水平的肺结节,从而将其转介给专科临床团队进行检查和潜在的手术干预。已经证明,在筛选试验中发现的这种结节通常是非常早期的疾病,因此这些患者具有非常好的临床结果。然而,结节在许多患者的扫描中很常见,经验丰富的放射科医生使用体积测量技术可以测量这些结节并确定它们是否正在生长。这些试验的数据已经开始改变对不确定的CT筛查检测到的结节的管理,从而证明了肺癌风险预测模型的力量,这将有助于目前正在讨论的如何实施肺癌CT筛查计划的方法[6]。1. Tammemagi, m.c.等,肺癌筛查的选择标准。中华医学杂志,2013。368(8):第728-36页。2. Field, j.k.等人,英国肺癌筛查试验:低剂量计算机断层扫描筛查早期发现肺癌的随机对照试验。卫生技术评估,2016。20(40): p. 1-146。3.Raji, o.y.等,利物浦肺项目风险模型对肺癌ct筛查患者分层的预测准确性:一项病例对照和队列验证研究。Ann Intern Med, 2012。157(4): p. 242-50。4. McWilliams, A.等,首次CT筛查发现肺结节的癌症可能性。中华医学杂志,2013。369(10): p. 910-9。5. Horeweg, N.等,CT检测到肺结节患者的肺癌概率:低剂量CT筛查NELSON试验数据的预先指定分析。柳叶刀肿瘤学杂志,2014。15(12): p. 1332-41。6. 菲尔德,j.k.,等。 CT筛查肺癌:证据足够有力吗?肺癌,2016。91: 29-35页。引文格式:John K. Field, Michael W. Marcus。肺癌风险预测建模:我们如何改进?[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要/ Abstract
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引用次数: 0
Abstract A27: Timing of pregnancies and oral contraceptive use and risk of ovarian cancer 摘要:妊娠时间和口服避孕药的使用与卵巢癌的风险
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-A27
C. Bodelón, H. Risch, F. Modugno, P. Webb, C. Pearce, M. Pike, N. Wentzensen
Background: Parity and use of oral contraceptive (OC) are associated with reduced risk of ovarian cancer. However, it is not clear whether these exposures have similar risk effects during different periods of life. In a large consortial analysis, we seek to evaluate the risk reductions associated with pregnancy and with OC use in different periods during the lifetime of a woman. Methods: We combined data from 17 population-based case-control studies of ovarian cancer that are part of the Ovarian Cancer Association Consortium (OCAC). Odds ratios (ORs) and 95% confidence intervals (CI) for associations between age of pregnancies and duration of OC use were estimated in individual studies using logistic regression and combined using random effects meta-analysis. Analyses were adjusted for age, duration of OC use, number of pregnancies, and race (Caucasian, Black, Asian and other). Studies that matched on ethnicity were additionally adjusted for Hispanic ethnicity (yes/no). All tests were two-sided and P-values less than 0.05 were considered statistically significant. Results: The analysis included 15,033 ovarian cancer cases and 25,312 controls. The median age for cases was 57 (interquantile range: 50-65) and 56 for controls (interquantile range: 48-64). Approximately 83.4% of cases and 88.5% of controls reported at least one pregnancy and 55.4% of cases and 61.7% of controls reported OCs use for at least one month. On average, each pregnancy was associated with a 17% reduced risk of ovarian cancer (OR=0.83, 95% CI: 0.8-0.85) while each year of OC use was associated with a 6% reduced risk (OR=0.94, CI 0.92-0.95). Among women who reported having at least one pregnancy, older age at last pregnancy was associated with lower risk of ovarian cancer (P Conclusions: In summary, older age at last pregnancy was significantly associated with reduced risk of ovarian cancer. There was a suggestion that older age at last used of OCs was associated with lower risk of ovarian cancer. These findings suggest that use of OCs to later ages in life can reduce ovarian cancer risk. A joint evaluation of life periods with pregnancies and OC use and ovarian cancer risk is under way. Citation Format: Clara Bodelon, Harvey Risch, Francesmary Modugno, Penelope Webb, Celeste Leigh Pearce, Malcolm Pike, Nicolas Wentzensen. Timing of pregnancies and oral contraceptive use and risk of ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A27.
背景:胎次和口服避孕药(OC)的使用与卵巢癌风险降低相关。然而,目前尚不清楚这些暴露是否在生命的不同时期有相似的风险影响。在一项大型联合分析中,我们试图评估女性一生中不同时期与妊娠和口服避孕药使用相关的风险降低。方法:我们结合了17个基于人群的卵巢癌病例对照研究的数据,这些研究是卵巢癌协会联盟(OCAC)的一部分。使用logistic回归和随机效应荟萃分析对个体研究中妊娠年龄与口服避孕药使用时间之间的比值比(ORs)和95%置信区间(CI)进行了估计。根据年龄、口服避孕药使用时间、怀孕次数和种族(高加索人、黑人、亚洲人和其他种族)对分析结果进行调整。种族匹配的研究还根据西班牙裔进行了调整(是/否)。所有检验均为双侧检验,p值小于0.05认为具有统计学意义。结果:分析包括15,033例卵巢癌病例和25,312例对照。病例的中位年龄为57岁(分位数范围:50-65),对照组为56岁(分位数范围:48-64)。大约83.4%的病例和88.5%的对照组报告至少怀孕一次,55.4%的病例和61.7%的对照组报告使用OCs至少一个月。平均而言,每次怀孕与卵巢癌风险降低17%相关(OR=0.83, 95% CI: 0.8-0.85),而每年使用OC与风险降低6%相关(OR=0.94, CI 0.92-0.95)。在报告至少怀孕一次的妇女中,最后一次怀孕的年龄较大与卵巢癌风险降低相关(P结论:总之,最后一次怀孕的年龄较大与卵巢癌风险降低显著相关。有一项研究表明,最后使用卵巢癌的年龄越大,患卵巢癌的风险就越低。这些发现表明,在晚年使用OCs可以降低患卵巢癌的风险。目前正在对怀孕和使用卵巢癌与卵巢癌风险的生命周期进行联合评估。引文格式:Clara Bodelon, Harvey Risch, Francesmary Modugno, Penelope Webb, Celeste Leigh Pearce, Malcolm Pike, Nicolas Wentzensen。怀孕时间和口服避孕药的使用与卵巢癌的风险。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr A27。
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引用次数: 0
Abstract A23: Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers A23:血浆骨保护素与BRCA1和BRCA2突变携带者乳腺癌风险的关系
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-A23
L. Salmena, L. Odén, S. Kim, M. Akbari, P. Sun, S. Narod, J. Kotsopoulos
Background: There is emerging evidence to suggest that progesterone-mediated upregulation of the receptor activator of nuclear factor κ β (RANK)/RANK ligand (RANKL) signaling pathway plays a critical role in mammary gland epithelial cell proliferation, mammary stem cell expansion and carcinogenesis. Of relevance for women at a high risk of developing breast cancer due to an inherited BRCA mutation, are recent findings showing that circulating levels of osteoprotegerin (OPG) (an endogenous decoy receptor for RANKL and thus inhibitor of RANK/RANKL-mediated signaling) are lower in women with a BRCA1 or BRCA2 mutation compared to non-carriers. Whether low OPG concentrations contribute to the high breast cancer risk in this population is unknown. If so, a therapeutic intervention that mimics the action of OPG might be used for primary prevention. We evaluated the relationship between plasma OPG and breast cancer risk among women with a BRCA1 or BRCA2 mutation in a prospective study. Methods: Baseline blood samples were available from 206 BRCA mutation carriers with no previous history of cancer. Plasma OPG concentrations were measured using a commercial enzyme-linked immunosorbent assay (ELISA) and categorized dichotomously as high vs. low based on the median of the entire cohort. The cumulative incidence of breast cancer by baseline plasma OPG concentration was estimated using Kaplan-Meier survival analysis. Results: Over a mean follow-up period of 6.5 years (range 0.1-18.8 years), 18 incident cases of primary invasive breast cancer were observed in the cohort. Women who developed breast cancer had significantly lower mean baseline OPG concentrations (90.59 pg/ml [range 4.2-205.7 pg/ml]) compared to the OPG concentrations of women who did not develop breast cancer ((117.9 pg/ml [7.4-547.7]) (P = 0.04). BRCA mutation carriers with low baseline OPG concentrations ( Conclusions: Our preliminary data suggest that low OPG concentrations are associated with an increased risk of breast cancer in BRCA1 and BRCA2 mutation carriers. These data support the potential for targeting of the RANKL pathway as a plausible cancer prevention strategy among women with germline BRCA mutations. Additional analyses with a larger sample size are underway and may help inform strategies of personalized prevention. These findings will not only further our understanding of the progesterone/OPG/RANKL pathway in breast cancer development, but will improve our identification of high-risk populations that can be targeted by prevention options that are currently available (i.e., denosumab) to simultaneously prevent breast cancer development and maintain bone health (particularly after salpingo-oophorectomy). Citation Format: Leonardo Salmena, Lovisa Oden, Shana Kim, Mohammad Akbari, Ping Sun, Steven Narod, Joanne Kotsopoulos. Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer
背景:越来越多的证据表明,孕激素介导的核因子κ β受体激活因子(RANK)/RANK配体(RANKL)信号通路的上调在乳腺上皮细胞增殖、乳腺干细胞扩增和癌变过程中起着关键作用。最近的研究结果显示,与非携带者相比,携带BRCA1或BRCA2突变的女性的循环骨保护素(OPG) (RANKL的内源性诱变受体,因此是RANK/RANKL介导信号的抑制剂)水平较低,这与遗传性BRCA突变导致乳腺癌高风险的女性相关。在这一人群中,低OPG浓度是否会导致高乳腺癌风险尚不清楚。如果是这样,一种模仿OPG作用的治疗干预可能用于一级预防。我们在一项前瞻性研究中评估了BRCA1或BRCA2突变女性血浆OPG与乳腺癌风险之间的关系。方法:对206例无癌症病史的BRCA突变携带者进行基线血液采集。使用商用酶联免疫吸附试验(ELISA)测量血浆OPG浓度,并根据整个队列的中位数分为高和低两类。通过基线血浆OPG浓度,使用Kaplan-Meier生存分析估计乳腺癌的累积发病率。结果:在平均6.5年的随访期间(0.1-18.8年),该队列中观察到18例原发性浸润性乳腺癌。与未患乳腺癌的女性相比,患乳腺癌的女性的OPG平均基线浓度(90.59 pg/ml[范围4.2-205.7 pg/ml])显著降低(117.9 pg/ml [7.4-547.7]) (P = 0.04)。结论:我们的初步数据表明,低OPG浓度与BRCA1和BRCA2突变携带者患乳腺癌的风险增加有关。这些数据支持了靶向RANKL通路作为一种可行的女性种系BRCA突变癌症预防策略的潜力。正在进行更大样本量的其他分析,可能有助于制定个性化预防策略。这些发现不仅将进一步加深我们对孕酮/OPG/RANKL通路在乳腺癌发展中的理解,而且将提高我们对高危人群的识别,这些高危人群可以通过现有的预防方案(即denosumab)来同时预防乳腺癌的发展和维持骨骼健康(特别是在输卵管卵巢切除术后)。引文格式:Leonardo Salmena, Lovisa Oden, Shana Kim, Mohammad Akbari, Ping Sun, Steven Narod, Joanne Kotsopoulos。血浆骨保护素与BRCA1和BRCA2突变携带者的乳腺癌风险[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr A23。
{"title":"Abstract A23: Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers","authors":"L. Salmena, L. Odén, S. Kim, M. Akbari, P. Sun, S. Narod, J. Kotsopoulos","doi":"10.1158/1538-7755.CARISK16-A23","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A23","url":null,"abstract":"Background: There is emerging evidence to suggest that progesterone-mediated upregulation of the receptor activator of nuclear factor κ β (RANK)/RANK ligand (RANKL) signaling pathway plays a critical role in mammary gland epithelial cell proliferation, mammary stem cell expansion and carcinogenesis. Of relevance for women at a high risk of developing breast cancer due to an inherited BRCA mutation, are recent findings showing that circulating levels of osteoprotegerin (OPG) (an endogenous decoy receptor for RANKL and thus inhibitor of RANK/RANKL-mediated signaling) are lower in women with a BRCA1 or BRCA2 mutation compared to non-carriers. Whether low OPG concentrations contribute to the high breast cancer risk in this population is unknown. If so, a therapeutic intervention that mimics the action of OPG might be used for primary prevention. We evaluated the relationship between plasma OPG and breast cancer risk among women with a BRCA1 or BRCA2 mutation in a prospective study. Methods: Baseline blood samples were available from 206 BRCA mutation carriers with no previous history of cancer. Plasma OPG concentrations were measured using a commercial enzyme-linked immunosorbent assay (ELISA) and categorized dichotomously as high vs. low based on the median of the entire cohort. The cumulative incidence of breast cancer by baseline plasma OPG concentration was estimated using Kaplan-Meier survival analysis. Results: Over a mean follow-up period of 6.5 years (range 0.1-18.8 years), 18 incident cases of primary invasive breast cancer were observed in the cohort. Women who developed breast cancer had significantly lower mean baseline OPG concentrations (90.59 pg/ml [range 4.2-205.7 pg/ml]) compared to the OPG concentrations of women who did not develop breast cancer ((117.9 pg/ml [7.4-547.7]) (P = 0.04). BRCA mutation carriers with low baseline OPG concentrations ( Conclusions: Our preliminary data suggest that low OPG concentrations are associated with an increased risk of breast cancer in BRCA1 and BRCA2 mutation carriers. These data support the potential for targeting of the RANKL pathway as a plausible cancer prevention strategy among women with germline BRCA mutations. Additional analyses with a larger sample size are underway and may help inform strategies of personalized prevention. These findings will not only further our understanding of the progesterone/OPG/RANKL pathway in breast cancer development, but will improve our identification of high-risk populations that can be targeted by prevention options that are currently available (i.e., denosumab) to simultaneously prevent breast cancer development and maintain bone health (particularly after salpingo-oophorectomy). Citation Format: Leonardo Salmena, Lovisa Oden, Shana Kim, Mohammad Akbari, Ping Sun, Steven Narod, Joanne Kotsopoulos. Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74159490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Abstract IA04: Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers' Study IA04:简化Rosner-Colditz乳腺癌发病率模型在加州教师研究中的验证
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-IA04
B. Rosner
Purpose: To validate a simplified breast cancer incidence model using baseline risk factors in an independent dataset Methods: We restricted the study population to comparable age ranges at baseline (age 47-79) (Nurses9 Health Study (NHS), 1994, n=64,627; California Teachers9 Study (CTS), 1995, n=31,386) We fit simplified Rosner-Colditz (RC) log incidence models using baseline risk factors and estimated both a 14-year risk model (1994-2008, 3597 cases) and a 4-year risk model (1994-1998, 1616 cases) based on NHS data. Both the 14-year and 4-year risk models were compared with the Gail model over the same time periods in the CTS population (14-year model, 1995-2009, 1786 cases; 4-year risk model, 1995-1999, 543 cases). We assessed performance using measures of discrimination based on AUC and calibration based on Poisson regression. Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA04.
目的:在独立数据集中验证使用基线危险因素的简化乳腺癌发病率模型方法:我们将研究人群限制在基线时可比较的年龄范围(47-79岁)(Nurses9健康研究(NHS), 1994, n=64,627;我们使用基线风险因素拟合简化的Rosner-Colditz (RC)对数发病率模型,并基于NHS数据估计14年风险模型(1994-2008年,3597例)和4年风险模型(1994-1998年,1616例)。将14年和4年风险模型与Gail模型在同一时期的CTS人群中进行比较(14年模型,1995-2009年,1786例;4年风险模型,1995-1999,543例)。我们使用基于AUC的判别方法和基于泊松回归的校准方法来评估性能。使用相关AUC方法(Rosner and Glynn, 2009)比较竞争模型的AUC。通过使用RC和Gail模型的相对风险以及SEER的绝对发病率来评估校准。结果:RC模型中考虑的变量有年龄;绝经年龄(按绝经类型)、绝经状态、初潮时的第1个出生年龄、无产、出生指数、良性乳腺疾病、当前HRT使用者按HRT类型使用HRT的持续时间、18岁时的体重、从18岁到基线的体重变化,分别按绝经状态和HRT使用、身高、饮酒量和乳腺癌的家族Hx。NHS人群的年龄调整AUC估计值为:(14年风险模型,RC模型:0.606±0.005,Gail模型:0.563±0.005,p diff diff =0.025)验证(CTS)人群的年龄调整AUC估计值为:(14年风险模型,RC模型:0.580±0.007,Gail模型:0.549±0.007,p diff diff =0.025)。校正14年风险模型表明,RC的估计E/O比为1.10 (95% CI = 1.05, 1.15);Gail为1.08 (95% CI = 1.05-1.13)。校正4年风险模型表明,RC的估计E/O比率为1.16 (95% CI = 1.07-1.26);Gail为1.15 (95% CI = 1.07-1.25)。用泊松回归校正结果相似。结论:基于基线危险因素的简化RC模型在临床环境中是实用的,并且在外部样本中验证时,其AUC明显高于Gail模型。AUC对短期(4年)风险预测优于长期风险预测。两种模型的校准都略有偏差,表明短期和长期模型的预期风险略高于观察到的风险。引用格式:Bernard A. Rosner。简化Rosner-Colditz乳腺癌发病率模型在加州教师研究中的验证[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr IA04。
{"title":"Abstract IA04: Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers' Study","authors":"B. Rosner","doi":"10.1158/1538-7755.CARISK16-IA04","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA04","url":null,"abstract":"Purpose: To validate a simplified breast cancer incidence model using baseline risk factors in an independent dataset Methods: We restricted the study population to comparable age ranges at baseline (age 47-79) (Nurses9 Health Study (NHS), 1994, n=64,627; California Teachers9 Study (CTS), 1995, n=31,386) We fit simplified Rosner-Colditz (RC) log incidence models using baseline risk factors and estimated both a 14-year risk model (1994-2008, 3597 cases) and a 4-year risk model (1994-1998, 1616 cases) based on NHS data. Both the 14-year and 4-year risk models were compared with the Gail model over the same time periods in the CTS population (14-year model, 1995-2009, 1786 cases; 4-year risk model, 1995-1999, 543 cases). We assessed performance using measures of discrimination based on AUC and calibration based on Poisson regression. Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA04.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75141975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Abstract B28: Clinicopathological features associated with late recurrence of gastric cancer 摘要B28:胃癌晚期复发的临床病理特征
Pub Date : 2017-05-01 DOI: 10.1158/1538-7755.CARISK16-B28
J. S. Park, C. Won, T. Son, Hyoung-il Kim, W. Hyung, S. Noh, T. Kim
Background: Because most cases of cancer recurrence occur within 5 years, routine surveillance is also recommended for first five years. However, few patients experience late recurrence of disease, and the mechanism of late recurrence is not clearly revealed. The purpose of this study is to evaluate the clinicopathological features predicting the risk of late recurrence in gastric cancer patients. Methods: From January 2006 to December 2013, we retrospectively reviewed 813 patients who were diagnosed and treated with gastric cancer in Yonsei cancer center. Result: Among 226 patients who experienced recurrence of gastric cancer, 212 patients were diagnosed with recurrence within first five years from the curative resection of primary cancer, and 14 patients were diagnosed with recurrence of disease beyond 5 years. In comparison with early recurrence (≤ 5 years), the patients with late recurrence (> 5 years) had primary disease of stage I/II (vs. stage III; HR, 4.5; 95% CI, 1.5-14.1; P=0.009), well or moderately differentiated histology (vs. poorly differentiated or signet ring cell; HR, 4.2; 95% CI, 1.4-13.1; P=0.013), and did not receive adjuvant chemotherapy (HR, 0.3; 95% CI, 0.1-0.9; P=0.028). All the 21 patients with HER2 positive gastric cancer experienced early recurrence. Conclusion: Late recurrence of gastric cancer is possibly not influenced by advanced stage of primary disease. More attempts to find high risk groups for late recurrence of gastric cancer are needed. Citation Format: Ji Soo Park, Chu Ree Won, Taeil Son, Hyoung-Il Kim, Woo Jin Hyung, Sung Hoon Noh, Tae Il Kim. Clinicopathological features associated with late recurrence of gastric cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B28.
背景:由于大多数癌症复发发生在5年内,建议前5年进行常规监测。然而,很少有患者出现疾病的晚期复发,晚期复发的机制尚不清楚。本研究的目的是评估预测胃癌晚期复发风险的临床病理特征。方法:对2006年1月至2013年12月在延世癌症中心确诊并治疗的813例胃癌患者进行回顾性分析。结果:226例胃癌复发患者中,212例原发癌治愈切除后5年内复发,14例5年以上复发。与早期复发(≤5年)相比,晚期复发(> 5年)患者原发疾病为I/II期(vs. III期;人力资源,4.5;95% ci, 1.5-14.1;P=0.009),良好或中度分化的组织学(相对于低分化或印戒细胞;人力资源,4.2;95% ci, 1.4-13.1;P=0.013),未接受辅助化疗(HR, 0.3;95% ci, 0.1-0.9;P = 0.028)。21例HER2阳性胃癌患者均出现早期复发。结论:胃癌晚期复发可能不受原发疾病晚期的影响。胃癌晚期复发的高危人群需要更多的尝试。引文格式:Ji Soo Park, Chu Ree Won, Taeil Son, Hyung -Il Kim, Woo Jin Hyung, Sung Hoon Noh, Tae Il Kim。胃癌晚期复发的临床病理特征分析。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr B28。
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引用次数: 0
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Cancer Epidemiology and Prevention Biomarkers
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