Mammographic density and texture features in relation to breast cancer risk
乳腺X线密度和纹理特征与乳腺癌风险的关系
基本信息
- 批准号:8741957
- 负责人:
- 金额:$ 35.21万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-30 至 2017-08-31
- 项目状态:已结题
- 来源:
- 关键词:AutomationBenignBiologyBreastBreast Cancer Risk FactorBreast DiseasesChemopreventionClinicalColumnar CellFilmGoalsHeterogeneityImageIndividualIntentionInterventionLeadLobularMammographic DensityMammographyManducaMeasurementMeasuresMediatingMenopausal StatusMethodsMolecularMotivationNursesNurses&apos Health StudyPathologyPatternPopulationPreventionProliferative Type Breast Fibrocystic ChangeReaderReportingResearchResearch PersonnelResourcesRiskRisk AssessmentSpecimenStatistical MethodsSurrogate MarkersTechniquesTextureTimeVariantWomanWorkbasebreast densitycancer riskdensitydigitalmalignant breast neoplasmnovelpublic health relevanceradiologistscreening
项目摘要
DESCRIPTION (provided by applicant): Mammographic density is one of the strongest risk factors for breast cancer. Despite this, the current measurement of breast density in the clinical setting (i.e., BI-RADS) is relatively subjective and utilization of this measure is minimal. The motivation for assessing BI-RADS is to alert radiologists because sensitivity of mammography is lower in women with dense breasts; the intention was not for risk assessment The most widely accepted research measure of mammographic density utilizes an operator-assisted technique based on the percentage of mammographic density (PMD). While these measures are well accepted to predict risk of breast cancer, they still require a reader which is both time intensive
and can lead to measurement error. The lack of automation is an impediment to clinical utilization. Further, there is additional information in mammographic images that are not captured by current PMD measurements. This heterogeneity in patterns of breast density is often referred to as 'texture'. We propose to evaluate the following three complementary automated measures of mammographic breast features in relation to subsequent breast cancer risk (Aim 1): (1) an automated measure of percent mammographic density, (2) individual texture measures and (3) a new measure, called V that captures a wide-band of textural information including spatial variation in a single measure. Each of these measures has demonstrated to predict breast cancer risk in at least one population. The three proposed measures developed by co-investigators are objective, automated techniques that are applicable to digitized film mammograms as well as digital mammograms. In Aim 2, we will evaluate breast cancer risk factor in relation to the texture features and will determine the extent to which breast cancer ris factors are mediated through mammographic density (i.e., automated PMD) and textural features (i.e., individual texture measures and V). Very little is known about the biology underlying mammographic texture features. We will determine if texture features on a mammogram are related to specific morphologic changes in the normal breast that are associated with breast cancer risk by examining these features on women whose benign breast disease specimens have undergone centralized pathology review (expected n=1304) (Aim 3). This proposal builds on a wealth of existing resources within the Nurses' Health Studies. As part of this study, we expect to have digitized screening film mammograms from 3480 breast cancer cases and 6974 controls. Because PMD is one of the strongest risk factors for breast cancer, a proposal to mandate the reporting of a relatively subjective non-automated measure of PMD, BI-RADS, to women undergoing screening is currently under Congressional review. The major goals of this proposal are to determine if automated measures of PMD and texture are associated with breast cancer, and to better understand the mechanisms by which they influence risk. Having automated and validated measures that strongly predict breast cancer risk has important implications for breast cancer risk prediction, screening, and chemoprevention.
描述(由申请人提供):乳腺摄影密度是乳腺癌最强的风险因素之一。尽管如此,目前在临床环境中对乳房密度的测量(即,BI-RADS)相对主观,并且该措施的利用率很低。评估BI-RADS的动机是提醒放射科医生,因为乳腺X线摄影的灵敏度在致密乳房的女性中较低;其目的不是进行风险评估。最广泛接受的乳腺X线摄影密度研究指标采用基于乳腺X线摄影密度百分比(PMD)的操作员辅助技术。虽然这些措施被广泛接受,以预测乳腺癌的风险,他们仍然需要一个读者,这是既费时
并且可能导致测量误差。缺乏自动化是临床应用的一个障碍。此外,在乳房X线摄影图像中存在当前PMD测量未捕获的附加信息。乳房密度模式的这种异质性通常被称为“纹理”。我们建议评估以下三种与后续乳腺癌风险相关的乳腺X线摄影乳腺特征的互补自动化测量(目标1):(1)乳腺X线摄影密度百分比的自动化测量,(2)个体纹理测量和(3)一种新的测量,称为V,其捕获宽带纹理信息,包括单个测量中的空间变化。这些措施中的每一个都被证明可以预测至少一个人群中的乳腺癌风险。共同研究者提出的三项建议措施是客观的、自动化的技术,适用于数字化电影乳房X线照片和数字化乳房X线照片。在目标2中,我们将评估与纹理特征相关的乳腺癌风险因素,并将确定乳腺癌风险因素通过乳房摄影密度介导的程度(即,自动PMD)和纹理特征(即,单独的纹理测量和V)。关于乳房X线摄影纹理特征的生物学基础知之甚少。我们将确定乳房X线照片上的纹理特征是否与正常乳房中与乳腺癌风险相关的特定形态学变化相关,方法是检查良性乳腺疾病标本经过集中病理学审查的女性的这些特征(预期n=1304)(目标3)。这项建议是建基于护士健康研究的现有资源。作为这项研究的一部分,我们预计将有3480例乳腺癌病例和6974例对照的数字化筛查胶片乳房X线照片。由于PMD是乳腺癌最强的风险因素之一,目前国会正在审查一项建议,要求对接受筛查的妇女报告相对主观的非自动PMD测量,BI-RADS。该提案的主要目标是确定PMD和纹理的自动测量是否与乳腺癌相关,并更好地了解它们影响风险的机制。具有自动化和验证的措施,可以强烈预测乳腺癌风险,对乳腺癌风险预测,筛查和化学预防具有重要意义。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Rulla M Tamimi其他文献
Gene × Gene interaction between MnSOD and GPX-1 and breast cancer risk: a nested case-control study
- DOI:
10.1186/1471-2407-6-217 - 发表时间:
2006-08-31 - 期刊:
- 影响因子:3.400
- 作者:
David G Cox;Rulla M Tamimi;David J Hunter - 通讯作者:
David J Hunter
Rulla M Tamimi的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Rulla M Tamimi', 18)}}的其他基金
Prediagnostic exposures, germline genetics, and triple negative breast cancer mutational and immune profiles
诊断前暴露、种系遗传学以及三阴性乳腺癌突变和免疫特征
- 批准号:
10596120 - 财政年份:2021
- 资助金额:
$ 35.21万 - 项目类别:
Computational pathology to predict breast cancer risk in benign breast disease
计算病理学预测良性乳腺疾病的乳腺癌风险
- 批准号:
9047258 - 财政年份:2015
- 资助金额:
$ 35.21万 - 项目类别:
Mammographic density and texture features in relation to breast cancer risk
乳房X线照相密度和纹理特征与乳腺癌风险相关
- 批准号:
8896563 - 财政年份:2013
- 资助金额:
$ 35.21万 - 项目类别:
Mammographic density and texture features in relation to breast cancer risk
乳腺X线密度和纹理特征与乳腺癌风险的关系
- 批准号:
8629862 - 财政年份:2013
- 资助金额:
$ 35.21万 - 项目类别:
Whole Genome Association Study of Mammographic Density
乳腺X线密度的全基因组关联研究
- 批准号:
8018197 - 财政年份:2009
- 资助金额:
$ 35.21万 - 项目类别:
Whole Genome Association Study of Mammographic Density
乳腺X线密度的全基因组关联研究
- 批准号:
7656493 - 财政年份:2009
- 资助金额:
$ 35.21万 - 项目类别:
Whole Genome Association Study of Mammographic Density
乳腺X线密度的全基因组关联研究
- 批准号:
7777342 - 财政年份:2009
- 资助金额:
$ 35.21万 - 项目类别:
Whole Genome Association Study of Mammographic Density
乳腺X线密度的全基因组关联研究
- 批准号:
8239989 - 财政年份:2009
- 资助金额:
$ 35.21万 - 项目类别:
相似海外基金
Genome analysis-based prediction model development for response to stereotactic radiosurgery in benign brain tumors
基于基因组分析的预测模型开发,用于良性脑肿瘤立体定向放射外科治疗的反应
- 批准号:
23K08495 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Clinical breast cancer risk prediction models for women with a high-risk benign breast diagnosis
高风险良性乳腺诊断女性的临床乳腺癌风险预测模型
- 批准号:
10719777 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Environmentally Benign Precise Transformations of Alkenes by Chiral Chalcogenide Catalysts
手性硫属化物催化剂对环境无害的烯烃精确转化
- 批准号:
22KJ2498 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Grant-in-Aid for JSPS Fellows
Learners to LeAders in benign Urology, benign Nephrology, and non-Cancer Hematology
良性泌尿外科、良性肾脏病学和非癌症血液学领域的学习者和领导者
- 批准号:
10726042 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
The role of estrogen receptor alpha in prostatic fibrosis contributing to benign prostatic hyperplasia
雌激素受体α在导致良性前列腺增生的前列腺纤维化中的作用
- 批准号:
10607151 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Development of a medical device to resolve benign esophageal stricture by heating and traction
开发通过加热和牵引解决良性食管狭窄的医疗设备
- 批准号:
23H03765 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Grant-in-Aid for Scientific Research (B)
Development of benign/malignant differentiation method for thyroid follicular tumor using organoids
利用类器官开发甲状腺滤泡性肿瘤良恶性鉴别方法
- 批准号:
23K08075 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Mechanisms of p53 Engagement and Action at the Benign-to-Malignant Transition in Sporadic Tumorigenesis
p53在散发性肿瘤发生良性向恶性转变中的参与和作用机制
- 批准号:
10720034 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
I-Corps: Mitigating Multidrug Resistant Bacterial Infections with Biocompatible and Environmentally Benign Nanoantibiotics
I-Corps:利用生物相容性且对环境无害的纳米抗生素减轻多重耐药细菌感染
- 批准号:
2306943 - 财政年份:2023
- 资助金额:
$ 35.21万 - 项目类别:
Standard Grant
Identifying the role of the gut microbiome in the etiology of benign breast disease
确定肠道微生物组在良性乳腺疾病病因学中的作用
- 批准号:
10359959 - 财政年份:2022
- 资助金额:
$ 35.21万 - 项目类别: