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.项目总结 乳腺癌是全世界最常见的癌症，也是美国人确诊的最常见的癌症。 女人。虽然在检测和治疗方法方面取得了很好的进展，但乳腺癌仍然存在 恶性肿瘤的主要死因。因此，对小说的发展有着迫切的需求 预测和预后因素。目前，风险评估是由医疗专业人员进行的，以确定 可以从加强乳房监测或降低风险方法中受益的妇女。不幸的是，大多数 确诊病例没有可识别的风险因素，这使得在感染前识别高危妇女成为一项挑战。 开始时使用经典的风险评估。这一医学难题导致了几种人工智能的发展 智能和机器学习方法被应用于乳房X光检查以识别乳腺癌 早些时候。然而，这些方法寻找表明存在癌症的异常情况，并已 发现不能推广到整个筛查人群。这在年轻女性中正变得越来越普遍 被诊断为乳腺癌，而且癌症往往更具侵袭性。此阶段第一阶段建议 创建用于乳房X光检查的风险评估产品，该产品不是基于机器学习，而是一种新的 测量有风险的致密组织。建筑和微环境组成的改变是一个很好的- 乳房病理的公认成分，某些变化可能发生在肿瘤发病之前。挥手 医学测量在识别易患肿瘤的致密组织区域时对这些变化很敏感。 这项可行性研究试图证明，对高风险致密乳房组织的新测量具有 有可能被应用到经典风险模型中。第一阶段的具体目标是：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光照片上的高风险致密组织 用于优化分析，以及2)建立高风险致密组织作为乳腺癌的更好预测因子 通过显示有风险的致密组织，乳房X光摄影百分比密度(MPD)在预测乳房方面更准确 癌症而不是MPD。第二阶段的后续工作将包括开发平台和将Waved集成到 评估乳房X光检查的医院基础设施。这些改进将创建一个风险评估产品 这提高了医疗专业人员识别高危患者的准确性，并确保患者 接受额外的医疗护理，如补充筛查或降低风险的方法，以防止侵入性 癌症。该项目的成功完成有可能推进最先进的乳腺癌评估 提供高风险致密组织的量化，以确定需要预防性护理的高危患者。