Novel Imaging Biomarkers for Guiding Personalized Screening Recommendations

该项目的目标是确定纳入乳腺组织的新成像标记物的益处 在指导个性化乳腺癌筛查建议的决策中的组合。我们特别 建议确定一个新的乳腺复杂性指数（BCI）的预测值，定义为 乳腺密度和实质纹理的综合描述符，在;）改善乳腺癌风险 估计和//）评估假阳性或假阴性筛查检查的风险。 确定乳腺密度是乳腺癌的一个重要的独立危险因素。此外，临床试验 显示乳腺致密女性的乳腺摄影敏感性和特异性显著下降， 与乳房脂肪的女性相比，这表明乳房密度的增加也是独立的， 与乳房X线照相术遗漏的癌症相关（即，假阴性）和假阳性增加 回忆道拟议的研究计划设计为分阶段成像生物标志物验证研究， 意图确定BCI在改善两个重要的个体化评估中的预测价值 个性化风险的方面：女性患乳腺癌的风险和她不必要的风险 召回或乳房X线检查隐匿性疾病。我们的主要假设是，通过整合定量乳腺癌 密度信息结合实质纹理特征，脑机接口可以提供更全面、客观的 乳腺组织成分的描述符比常用的BIRADS密度和乳腺百分比密度 (PD%）的估计值，从而在个体水平上产生更准确的预测。改善风险 这两方面的评估可以为指导乳腺癌筛查做出更明智的决定 建议，认识到个性化的筛查方法不仅加强了监测， 那些最有可能受益的人（即，高风险妇女），但也应减少强度或提供替代办法 接近那些不太可能受益的人。在这种情况下，我们建议比较乳房X光检查和乳房 断层合成，一种新兴的断层乳腺成像方式，有可能显着减少 筛查中的假阳性回调，特别是对于乳腺组织非常复杂的女性。我们 这项研究有望通过提供改善的乳房保健服务， 指导个性化乳腺癌筛查建议的临床决策工具。

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.