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Prediction Model: Breast Cancer in Women Irradiated for a Pediatric Malignancy

Prediction Model: Breast Cancer in Women Irradiated for a Pediatric Malignancy
预测模型:因儿童恶性肿瘤接受放射治疗的女性乳腺癌
批准号:
8433993
负责人:
CHAYA MOSKOWITZ
金额:
$34.77万
依托单位国家:
美国
项目类别:
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-04-01 至 2015-01-31

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项目成果

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中文摘要
翻译
描述(由申请人提供):儿科恶性肿瘤的幸存者面临与其治疗相关的严重长期发病和过早死亡的风险。因儿童期癌症接受胸部放射治疗的女性在年轻时患乳腺癌的风险会增加。我们使用术语胸部辐射来指代包括以下领域的辐射:地幔、纵隔、肺、全身辐射或脊柱。 预测乳腺癌绝对风险的模型,例如著名的盖尔模型,已被广泛用于向患者提供有关其患乳腺癌的个人风险的建议并设计预防试验。这些风险计算器中的大多数并不能立即适用于先前患有恶性肿瘤的幸存者,他们的风险因先前的治疗而有所改变。此外,关于各种非治疗相关因素(包括传统乳腺癌危险因素)是否与这些女性患乳腺癌的风险相关,存在相互矛盾的证据。传统的危险因素包括年龄、初潮年龄、第一胎出生年龄、患有乳腺癌的一级亲属人数、既往乳腺活检次数以及非典型增生病史。 我们建议利用北美儿童癌症幸存者研究 (CCSS) 和荷兰 LATe 效应登记 (LATER) 队列的独特资源。我们的首要目标是开发一个风险预测模型,整合胸部放射剂量和体积、治疗相关暴露、传统风险因素以及其他可能的风险因素,如绝经年龄、体重指数、放疗后卵巢功能完好的年数、激素替代或口服避孕药的使用,以估计因儿童癌症接受胸部放射治疗的女性患乳腺癌的个体化绝对风险。该模型将使用原始 CCSS 队列进行开发,其中有 1,677 名女性参与者接受了胸部放射治疗,并将纳入本次分析。在这 1,677 名女性中,有 187 名女性在提交本申请时已患乳腺癌。我们的第二个目标是验证独立数据的预测模型。验证队列将由扩大的 CCSS 队列中的约 1225 名女性参与者和 Dutch LATER 队列中的 1087 名女性参与者组成。我们估计其中大约 100 名女性将患有乳腺癌。第三个目标是创建并传播提供计算机辅助风险预测的风险计算器。我们的目标是以易于访问的形式提供一种工具,并将其传播给医生和患者进行临床使用。该项目的长期目标是提供一种手段,促进医生与患者之间就适当的筛查和预防策略进行对话,并帮助完善针对该人群的筛查建议。
英文摘要
DESCRIPTION (provided by applicant): Survivors of a pediatric malignancy are at risk for serious long-term morbidity and premature mortality related to their treatment. Women who were treated with chest radiation for their childhood cancer have an increased risk of developing breast cancer at a young age. We use the term chest radiation to refer to radiation that includes the following fields: mantle, mediastinal, lung, total body irradiation, or spinal. Models for predicting the absolute risk of breast cancer, such as the well-known Gail model, have been used extensively to advise patients on their individual risk of developing breast cancer and to design prevention trials. The majority of these risk calculators are not immediately applicable to survivors of a previous malignancy who have a risk that is modified by previous treatments. Moreover, there is conflicting evidence as to whether various non-treatment related factors, including the traditional breast cancer risk factors, are associated with the risk of breast cancer in these women. The traditional risk factors include age, age at menarche, age at birth of first live child, number of first-degree relatives with breast cancer, number of previous breast biopsies, and history of atypical hyperplasia. We propose to utilize the unique resources of the North American Childhood Cancer Survivor Study (CCSS) and the Dutch LATe Effect Registry (LATER) cohorts. Our first aim is to develop a risk prediction model that integrates chest radiation dose and volume, treatment-related exposures, the traditional risk factors, and other possible risk factors such as age at menopause, body mass index, the number of years with intact ovarian function after radiotherapy, and use of hormone replacement or oral contraceptive therapy, in order to estimate the individualized absolute risk of breast cancer for women who were treated with chest radiation for a childhood cancer. The model will be developed using the original CCSS cohort in which there are 1,677 female participants who were treated with chest radiation and would be included in this analysis. Of these 1,677 women, 187 women had developed breast cancer by the time of this application. Our second aim is to validate the prediction model on independent data. The validation cohort will consist of about 1225 female participants in the expanded CCSS cohort and 1087 female participants in the Dutch LATER cohort. We estimate that approximately 100 of these women will have breast cancer. The third aim is to create and disseminate a risk calculator that provides computer-assisted risk prediction. Our objective is to present a tool in an easily accessible format and disseminate it for clinical use by physicians and patients. The long-term goals of this project are to provide a means for facilitating conversations between physicians and their patients about appropriate screening and preventive strategies and to help refine screening recommendations for this population.
期刊论文(4)
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会议论文
DOI: 10.1002/sim.5648
发表时间: 2013-04-30
期刊: STATISTICS IN MEDICINE
影响因子: 2
作者: [Seshan, Venkatraman E., Goenen, Mithat, Begg, Colin B.]
通讯作者: Begg, Colin B.
Prediction Model: Breast Cancer in Women Irradiated for a Pediatric Malignancy
Prediction Model: Breast Cancer in Women Irradiated for a Pediatric Malignancy
Prediction Model: Breast Cancer in Women Irradiated for a Pediatric Malignancy
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