Image-based risk assessment to identify women at high-risk for breast cancer

基于图像的风险评估可识别乳腺癌高危女性

• 批准号: 10759110
• 负责人: Kendra Batchelder
• 金额: $ 40.6万
• 依托单位: WAVED MEDICAL LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10759110
• 关键词: Accounting; Address; Adipose tissue; Algorithms; American; Architecture; Area; Artificial Intelligence; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Breast Cancer Risk Factor; Caring; Cause of Death; Clinical; Computer software; Data; Databases; Detection; Development; Diagnosis; Early Diagnosis; Ensure; Feasibility Studies; Funding; Grant; Health care facility; High Risk Woman; Hospitals; Image; Incidence; Infrastructure; Legal patent; Life Style; Link; Machine Learning; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Manuals; Measurement; Measures; Medical; Medical Device; Medical Imaging; Medical Records; Methods; Modeling; Patient risk; Patient-Focused Outcomes; Patients; Performance; Phase; Population; Positioning Attribute; Predictive Factor; Preventive care; Process; Prognostic Factor; Protocols documentation; Questionnaires; ROC Curve; Reader; Recommendation; Research; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Reduction; Scanning; Screening procedure; Secure; Small Business Technology Transfer Research; Teaching Method; Technology; Technology Assessment; Time; Tissues; Validation; Visit; Visualization; Woman; Work; aged; automated analysis; breast density; breast pathology; cancer diagnosis; cancer invasiveness; clinical practice; cyber security; density; design; digital; high risk; imaging modality; improved; innovation; innovative technologies; malignant breast neoplasm; novel; novel strategies; phase 1 study; prevent; risk stratification; routine screening; screening; supplemental screening; timeline; tumor; young woman

7. PROJECT SUMMARY Breast cancer is the most common cancer worldwide and the most common cancer diagnosed in American women. While there has been good progress regarding detection and treatment methods, breast cancer remains the primary cause of death from malignant tumors. Hence, there is a critical need for the development of novel predictive and prognostic factors. Risk assessments are currently performed by medical professionals to identify women that could benefit from enhanced breast surveillance or risk reduction methods. Unfortunately, most diagnosed cases do not have an identifiable risk factor, making it a challenge to identify high risk women prior to onset using classical risk assessments. This medical difficulty has resulted in the development of several artificial intelligence and machine learning approaches being applied to screening mammograms to identify breast cancer earlier. However, these approaches search for abnormalities that indicate an existing cancer and have been found to not be generalizable to the entire screening population. It is becoming more common for younger women to be diagnosed with breast cancer, and the cancers tend to be more aggressive. This Phase I proposes to create a risk assessment product for mammography that is not based on machine learning but rather a novel measurement of risky dense tissue. Alteration in the architecture and composition of microenvironment is a well- recognized component of breast pathologies and some changes may occur prior to tumor onset. WAVED Medical’s measurement is sensitive to these alternations in identifying areas of dense tissue that is tumor prone. This feasibility study seeks to demonstrate that the novel measurement of risky dense breast tissue has the potential to be implemented into classical risk models. Phase I specific aims are to 1) improve efficiency in identifying risky dense tissue on mammograms by creating a secure database that contains preprocessed data for optimized analysis, and 2) establish risky dense tissue as a better predictor of breast cancer than traditional mammographic percent density (MPD), by showing risky dense tissue is more accurate in predicting breast cancer than MPD. Follow-on Phase II efforts will include developing a platform and integrating WAVED into hospital infrastructure for evaluating mammograms. These improvements will create a risk assessment product that increases the accuracy of medical professionals at identifying high-risk patients and ensures patients are receiving additional medical care, such as supplemental screening or risk reduction methods, to prevent invasive cancer. Successful completion of the project has potential to advance state-of-the-art breast cancer assessments to provide quantification of risky dense tissue to identify high-risk patients needing preventive care.

7。项目摘要 乳腺癌是全球最常见的癌症，也是美国诊断出的最常见的癌症 女性。尽管在检测和治疗方法方面取得了良好的进展，但乳腺癌仍然存在 恶性肿瘤死亡的主要原因。因此，对新颖的发展有迫切的需求 预测性和预后因素。目前，医疗专业人员目前进行风险评估以识别 可能受益于增强乳房监测或降低风险方法的女性。不幸的是，大多数 被诊断的病例没有可识别的危险因素，这使得在识别高风险女性之前是一个挑战 使用经典风险评估发作。这种医疗困难导致了几种艺术的发展 智力和机器学习方法应用于筛查乳房X线照片以识别乳腺癌 较早。但是，这些方法寻找表明现有癌症的异常，并且已经 发现无法推广到整个筛查人群。对于年轻女性来说，它变得越来越普遍 被诊断出患有乳腺癌，癌症往往更具侵略性。我提出的这个阶段 为乳房X线摄影创建风险评估产品，而不是基于机器学习，而是基于新颖的 测量风险密集的组织。微环境的结构和组成的改变是一个很好的 乳腺病理的公认成分和一些变化可能发生在肿瘤发作之前。挥舞着 医学的测量对这些替代方案敏感，以识别易于肿瘤的密集组织区域。 这项可行性研究旨在证明风险密集的乳腺组织的新型测量 潜力被实施到经典风险模型中。第一阶段的特定目的是1）提高效率 通过创建包含预处理数据的安全数据库来识别乳房X线照片上有风险的密集组织 进行优化的分析，以及2）建立风险的密集组织作为乳腺癌的更好预测指标 乳房X线摄影百分比密度（MPD），通过显示危险的密集组织在预测乳房方面更为准确 癌症比MPD。后续第二阶段的工作将包括开发一个平台并将波整合到 用于评估乳房X线照片的医院基础设施。这些改进将创建风险评估产品 这提高了医疗专业人员在识别高危患者方面的准确性，并确保患者是 接受额外的医疗服务，例如补充筛查或降低风险方法，以防止侵入性 癌症。成功完成该项目有可能提高最先进的乳腺癌评估 为了提供数量的风险密集组织以识别高危患者需要预防性护理。