Novel Imaging Biomarkers for Guiding Personalized Screening Recommendations

用于指导个性化筛查建议的新型成像生物标志物

• 批准号: 8555348
• 负责人: KATRINA ARMSTRONG
• 金额: $ 16.5万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-21 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8555348
• 关键词: Accounting; Adverse effects; Advisory Committees; Affect; Age; Area; Back; Biological Markers; Breast; Breast Cancer Detection; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Cataract; Clinical; Clinical Trials; Complex; Descriptor; Digital Mammography; Disease; Drops; Endometrial Carcinoma; General Population; Goals; High Risk Woman; Image; Individual; Intention; Lesion; Logistic Regressions; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Measures; Methods; Modeling; Odds Ratio; Outcome; Patients; Phase; Population; Predictive Value; Prevention strategy; Protocols documentation; Recommendation; Regimen; Research; Risk; Risk Assessment; Risk Factors; Screening procedure; Sensitivity and Specificity; Stroke; Testing; Texture; Tissues; Woman; base; breast density; cancer risk; case control; clinical decision-making; clinically relevant; density; design; digital; follow-up; health care delivery; imaging modality; improved; indexing; innovation; malignant breast neoplasm; novel; primary outcome; reproductive; secondary outcome; statistics; tool; validation studies

The goal of this project is to determine the benefit of incorporating novel imaging markers of breast tissue composition in guiding decisions for personalized breast cancer screening recommendations. Specifically, we propose to determine the predictive value of a new Breast Complexity Index (BCI), defined as a comprehensive descriptor of breast density and parenchymal texture, in ;) improving breast cancer risk estimation and //) assessing the risk of a false-positive or a false-negative screening exam. Studies have identified breast density as a strong independent risk factor for breast cancer. In addition, clinical trials have shown that mammographic sensitivity and specificity drops significantly in women with dense breasts, when compared to women with fatty breasts, suggesting that increased breast density is also independently associated with cancers missed by mammography (i.e., false negatives) and an increase in false positive recalls. The proposed research plan is designed as a phased imaging biomarker validation study with the Intention to determine the predictive value of BCI in improving individualized assessment in two important aspects of personalized risk: a woman's risk of developing breast cancer and her risk of having unnecessary call-back or mammographically occult disease. Our main hypothesis is that, by integrating quantitative breast density information with parenchymal texture features, BCI can provide a more comprehensive and objective descriptor of breast tissue composition than the commonly used BIRADS density and breast percent density (PD%) estimates and therefore result in more accurate predictions at the individual level. Improving risk assessment in both of these aspects can result in more informed decisions for guiding breast cancer screening recommendations, recognizing that personalized approaches to screening not only intensify surveillance to those most likely to benefit {i.e., high-risk women), but should also reduce intensity or provide alternative approaches to those less likely to benefit. Within this setting we propose compare mammography to breast tomosynthesis, an emerging tomographic breast imaging modality with the potential to significantly reduce false-positive call-backs in the screening setting, especially for women with very complex breast tissue. Our study holds the promise to shift the current paradigm in breast health care delivery by providing improved clinical decision-making tools for guiding personalized breast cancer screening recommendations.

该项目的目的是确定纳入乳腺组织的新成像标记的好处 指导个性化乳腺癌筛查建议的决定。具体来说，我们 建议确定新乳腺复杂性指数（BCI）的预测值，该指数定义为 乳房密度和实质质地的全面描述符，in;）改善乳腺癌风险 估计和//）评估假阳性或假阴性筛查考试的风险。研究有 确定乳房密度是乳腺癌的强大独立危险因素。此外，临床试验具有 表明，当乳房密集的女性中，乳房X线X线摄影敏感性和特异性显着下降 与脂肪乳房的女性相比，表明乳房密度增加也是独立的 与因乳房X线摄影（即假阴性）错过的癌症和假阳性的增加相关 回忆。拟议的研究计划被设计为一项分阶段成像生物标志物验证研究 打算确定BCI在改善两个重要评估的个性化评估方面的预测价值 个性化风险的各个方面：妇女患乳腺癌的风险和不必要的风险 呼叫或乳房X线神秘性疾病。我们的主要假设是，通过整合定量乳房 BCI具有实质质地特征的密度信息，可以提供更全面和客观的 乳腺组织成分的描述符，而不是常用的Birads密度和乳房百分比密度 （PD％）估计值，因此在单个级别上产生更准确的预测。改善风险 这两个方面的评估都可以导致更明智的决定来指导乳腺癌筛查 建议，认识到筛查的个性化方法不仅会加剧监视 最有可能受益的人（即高风险妇女），但也应降低强度或提供替代性 处理那些不太可能受益的人的方法。在这种情况下，我们建议将乳房X线摄影与乳房进行比较 Tomosynthesis，一种新兴的层析成像乳房成像方式，具有显着降低的潜力 筛查环境中的假阳性呼叫背部，特别是对于乳房组织非常复杂的女性。我们的 研究有望通过提供改进来改变乳腺保健服务中当前的范例 用于指导个性化乳腺癌筛查建议的临床决策工具。