I-Corps: Customer discovery for breast cancer detection technology

这个I-Corps项目更广泛的影响/商业潜力是开发软件，仅使用患者的第一次（基线）乳房X线照片创建和推荐个性化乳房筛查计划。这种个性化护理可能会限制患者的辐射暴露，或者为患者提供补充筛查。改善年轻妇女的筛查过程为更好的预后提供了机会。此外，早期检测可以允许新的治疗选择，以靶向易患癌症的乳腺组织，可能导致疾病的预防。 该软件将与乳腺癌风险基因检测具有可比性和互补性，I-Corps项目的基础是开发一种拟议的计算机辅助检测软件，并利用该软件区分健康、有组织、致密的乳房X线摄影乳腺组织和通常与肿瘤发生有关的不健康、无组织、致密的乳腺组织。健康组织和不健康组织之间的细微差别很难在视觉上识别。该技术区分健康和不健康的组织，以识别乳房X线摄影乳腺组织中特别“危险”的子区域。 这项技术可以解释乳腺密度对乳腺癌风险增加的影响。计算机辅助乳腺癌检测软件已经使用了二十多年，但没有显著的临床改进。现有的计算机辅助检测程序已被医疗专业人士批评为产生大量的假阳性，导致不必要的活检和增加的评估时间。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of software to create and recommend personalized breast screening plans, using only the patient’s first (baseline) mammogram. This individualized care could result in limiting the patient’s exposure to radiation or could provide the patient with access to supplemental screening. Improving the screening process for women at a young age offers the chance of a better prognosis. In addition, early detection could allow for novel treatment options to target cancer-prone breast tissue, potentially leading to the prevention of the disease. The software would be comparable and complementary to genetic testing for breast cancer risks.This I-Corps project is based on the development of a proposed computer-aided detection software and its potential to discriminate between healthy, organized, dense mammographic breast tissue versus unhealthy, disorganized, dense breast tissue often associated with the onset of a tumor. The subtle differences between healthy and unhealthy tissue are challenging to identify visually. This technology discriminates between healthy and unhealthy tissue to identify the sub-regions of mammographic breast tissue that are particularly “risky”. The technology may explain the effect breast density has on someone's increased risk for breast cancer. Computer-aided breast cancer detection software has been used for over two decades without signficant clinical improvement. The existing computer-aided detection programs have been criticized by medical professionals for producing high numbers of false positives, resulting in unnecessary biopsies and increased evaluation times. The proposed technology may solve these problems, offering a less stressful option to patients and their families.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.