Novel Imaging Biomarkers for Guiding Personalized Screening Recommendations

该项目的目标是确定结合新的乳腺组织成像标记的益处。 在指导个性化乳腺癌筛查建议决策方面的构成。具体来说，我们 建议确定新的乳房复杂性指数(BCI)的预测价值，该指数定义为 乳房密度和实质质地的综合描述，in；)提高乳腺癌风险 估计和//)评估假阳性或假阴性筛查检查的风险。研究表明， 确定乳房密度是乳腺癌的一个强烈的独立风险因素。此外，临床试验已经 显示乳房致密的女性乳房X光检查的敏感度和特异度显著下降 与乳房肥大的女性相比，乳房密度的增加也是独立的 与乳房X光检查漏诊的癌症(即假阴性)和假阳性增加有关 召回。拟议的研究计划被设计为阶段性成像生物标志物验证研究， 意向判断脑-机接口在改善个性化评估中的预测价值 个性化风险的方方面面：女性患乳腺癌的风险和不必要的风险 回叫或乳房X光检查中的隐匿性疾病。我们的主要假设是，通过整合定量的乳房 结合密度信息和实质纹理特征，可以提供更全面、客观的脑机接口 乳房组织成分描述符比常用的BIRADS密度和乳房百分比密度 (PD%)估计，从而在个人层面上产生更准确的预测。改善风险 在这两个方面的评估可以导致更明智的决定，以指导乳腺癌筛查 建议，认识到个性化的筛查方法不仅加强了对 那些最有可能受益的人(即高危妇女)，但也应降低强度或提供替代方案 针对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光检查与乳房进行比较 断层合成，一种新兴的胸部断层成像方式，有可能显著降低 筛查环境中的假阳性回电，特别是对于乳房组织非常复杂的女性。我们的 这项研究承诺通过提供改进的乳房保健提供来改变目前的乳房保健交付模式 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.