Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications

动态成像和组织生物标志物模型可区分惰性乳腺钙化和侵袭性乳腺钙化

• 批准号: 10704546
• 负责人: Lars J Grimm
• 金额: $ 45.86万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10704546
• 关键词: Address; Appearance; Benign; Biological; Biological Markers; Biology; Biopsy; Breast; Breast Cancer Detection; Calibration; Cancer Biology; Classification; Clinical; Clinical Management; Clinical Trials; Computer Assisted; Computer Vision Systems; Creativeness; Data; Databases; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Dimensions; Disease; Disease Progression; Epigenetic Process; Evolution; Excision; Failure; Fostering; Foundations; Goals; Growth; Harm Reduction; Health; Health Care Costs; Histopathology; Image; In Situ Lesion; Indolent; Joints; Lead; Lesion; Malignant Neoplasms; Mammographic screening; Mammography; Mathematics; Measures; Methods; Mission; Modality; Modeling; Monitor; Morbidity - disease rate; Noninfiltrating Intraductal Carcinoma; Operative Surgical Procedures; Outcome; Pathology; Patient Monitoring; Patient risk; Patients; Pattern; Performance; Practice Guidelines; Predictive Value; Prognosis; Public Health; Research; Resources; Risk; Surgical Oncology; Testing; Time; Tissue Sample; Tissues; Uncertainty; United States; United States National Institutes of Health; Woman; Work; breast lesion; calcification; clinical decision-making; clinical practice; cohort; follow-up; imaging biomarker; improved; infiltrating duct carcinoma; innovation; malignant breast neoplasm; molecular clock; multidisciplinary; neoplastic; overtreatment; personalized medicine; predictive marker; prognostic; prognostic model; prospective; psychological distress; radiological imaging; radiologist; radiomics; risk stratification; screening; screening guidelines; screening program; serial imaging; tissue biomarkers; treatment planning; tumor

ABSTRACT. Breast cancer screening programs suffer from false positive mammograms, unnecessary biopsies, overdiagnosis, and overtreatment. A major contributor to the poor performance of screening mammography is the diagnostic and prognostic uncertainty of mammographically detected calcifications. Breast calcifications represent a biological continuum from benign disease to ductal carcinoma in situ (DCIS) to aggressive cancer. Radiologists struggle to correlate their imaging appearance with the underlying pathology and roughly two-thirds of biopsied calcifications return with a benign pathology. Although calcifications evolve dynamically over time, the current management strategy relies heavily on the static appearance of calcifications from the most recent mammogram. Most women in screening programs have multiple mammograms, yet this temporal information is consistently underutilized in clinical decision making. There is thus an urgent need to quantify the dynamics of calcifications from serial mammograms, and to characterize the relationship between calcification trajectories and disease biology. In the absence of such innovation, increasingly sensitive screening modalities are expected to further increase the burden of unnecessary diagnostic work-up and breast cancer overdiagnosis. The central hypothesis of this proposal is that dynamic imageable and tissue biomarkers contain actionable diagnostic and prognostic information about mammographic calcifications. The use of established diagnostic imaging (mammography) in conjunction with investigational imageable biomarkers will enable testing of this hypothesis. Key to this proposal will be the creation of a large database of retrospectively and prospectively collected cohorts of patients with serial mammograms, tissue samples and clinical outcomes. This proposal will consist of three specific aims: (1) Develop a static model of breast calcifications to improve the clinical performance of mammography screening; 2) Develop a dynamic model of breast calcifications to predict histopathology and DCIS prognosis; and 3) Combine the dynamic calcification model with tissue-based biomarkers of the underlying evolutionary dynamics to delineate DCIS prognosis. The proposed research is highly innovative because it adds the temporal dimension to computer-assisted classification of mammographic calcifications, yields a joint characterization of calcification growth trajectories and lesion biology, and develops dynamic risk models to predict invasive progression in women undergoing active monitoring for DCIS. This proposal will be co-led by Dr. Grimm (breast radiologist) and Dr. Ryser (mathematical modeler) supported by a highly collaborative multidisciplinary team with expertise in cancer biology, computer vision, and surgical oncology. The overall objective of this proposal is to develop a dynamic imageable biomarker that delineates lethal cancer from non- lethal disease by leveraging the temporal dimension of serial mammograms. Ultimately, the long-term goal of our work is to better identify which calcifications to biopsy (reduce unnecessary biopsies), and if pre-invasive DCIS is found, to predict whether it will remain indolent or progress to lethal cancer (reduce overtreatment).

抽象的。乳腺癌筛查项目遭受假阳性乳房X光检查，不必要的活检， 过度诊断和过度治疗。筛查乳房X光检查表现不佳的一个主要原因是 乳房X光检查发现钙化的诊断和预后不确定性。乳房钙化 代表了从良性疾病到导管原位癌(DCIS)再到侵袭性癌症的生物学连续体。 放射科医生努力将他们的影像表现与潜在的病理联系起来，大约三分之二 活组织检查的钙化带回良性病变。尽管钙化会随着时间的推移动态演变， 目前的管理策略很大程度上依赖于最近钙化的静态外观 乳房X光检查。参加筛查计划的大多数女性都有多次乳房X光检查，但这些时间信息是 在临床决策中始终未得到充分利用。因此，迫切需要量化 从连续的乳房X线片中发现钙化，并表征钙化轨迹之间的关系 和疾病生物学。在没有这种创新的情况下，预计会有越来越敏感的筛查模式 进一步增加不必要的诊断工作和乳腺癌过度诊断的负担。中环 这一建议的假设是，动态可成像和组织生物标记物包含可操作的诊断和 关于乳房X光摄影钙化的预后信息。使用已建立的诊断成像 (乳房X光摄影)与可调查的可成像生物标志物相结合，将使这一假说得到验证。 这一建议的关键将是创建一个大型数据库，其中包括回顾和预期收集的队列 对患者进行一系列的乳房X光检查、组织样本和临床结果。这项提案将由三个部分组成 具体目标：(1)建立静态的乳房钙化模型，以提高临床表现 乳房X光摄影筛查；2)建立乳房钙化动态模型以预测组织病理学和 DCIS的预后；以及3)结合动态钙化模型和基于组织的潜在生物标志物 进化动力学对DCIS预后的预测。拟议的研究具有很高的创新性，因为它增加了 计算机辅助分类乳房X线片钙化的时间维度产生关节 表征钙化生长轨迹和病变生物学，并开发动态风险模型以 预测接受DCIS积极监测的妇女的侵袭性进展。该提案将由以下人员共同领导 格里姆博士(乳腺放射科医生)和赖瑟博士(数学建模专家)得到了高度合作的支持 拥有癌症生物学、计算机视觉和外科肿瘤学专业知识的多学科团队。整体而言 这项提案的目标是开发一种动态可成像的生物标记物，以区分致命的癌症和非致命性的癌症。 致命疾病通过利用连续乳房X光照片的时间维度。最终，长期目标是 我们的工作是更好地确定哪些钙化应该进行活组织检查(减少不必要的活组织检查)，以及是否要进行侵入性检查 发现DCIS，以预测它是否会继续懒惰或进展为致命的癌症(减少过度治疗)。