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: 10448752
• 负责人: Lars J Grimm
• 金额: $ 40.31万
• 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/10448752
• 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; Conflict (Psychology); Data; Databases; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Dimensions; Disease; Disease Progression; Epigenetic Process; Evolution; Excision; Failure; Fostering; Foundations; Goals; Growth; Health; Health Care Costs; Histopathology; Image; In Situ Lesion; Indolent; Investigation; 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; Plant Roots; 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; base; breast lesion; calcification; clinical decision-making; clinical practice; cohort; follow-up; imaging biomarker; improved; 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).

摘要。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乳腺X线筛查表现不佳的一个主要原因是 乳腺X线检查发现钙化的诊断和预后不确定性。乳腺钙化 代表从良性疾病到导管原位癌（DCIS）再到侵袭性癌症的生物连续体。 放射科医生很难将他们的成像表现与潜在的病理学联系起来，大约三分之二的人 活检钙化返回良性病理。尽管钙化随时间动态演变， 目前的管理策略严重依赖于最近钙化的静态外观 乳房X光片大多数接受筛查的妇女都有多次乳房X光检查，但这种时间信息是不确定的。 在临床决策中一直未得到充分利用。因此，迫切需要量化 钙化，并表征钙化轨迹之间的关系， 和疾病生物学。在缺乏这种创新的情况下，预计会出现越来越敏感的筛查方式 进一步增加不必要的诊断检查和乳腺癌过度诊断的负担。中央 该提议假设是动态可成像的和组织生物标记物包含可操作的诊断和 乳腺摄影钙化的预后信息。使用已建立的诊断成像 （乳房X线摄影）结合研究性可成像生物标志物将能够检验该假设。 这项建议的关键是建立一个回顾性和前瞻性收集的队列的大型数据库 患者的连续乳房X线照片、组织样本和临床结果。该提案将包括三个 具体目标：（1）开发乳腺钙化的静态模型，以提高临床性能， 乳房X线摄影筛查; 2）开发乳房钙化的动态模型以预测组织病理学， DCIS预后;以及3）将动态钙化模型与潜在DCIS的基于组织的生物标志物相结合。 描述DCIS预后的进化动力学。拟议的研究具有高度创新性，因为它增加了 计算机辅助分类的乳房X线摄影钙化的时间维度，产生联合 表征钙化生长轨迹和病变生物学，并开发动态风险模型， 预测接受DCIS主动监测的女性的浸润性进展。该提案将由以下人员共同领导： 博士Grimm（乳腺放射科医生）和Ryser博士（数学建模师）在高度合作的支持下， 该团队拥有癌症生物学、计算机视觉和外科肿瘤学方面的专业知识。整体 该提议的目的是开发动态可成像生物标志物，其描绘致死性癌症与非致死性癌症， 致命的疾病通过利用序列乳房X光片的时间维度。最终，我们的长期目标是 我们的工作是更好地识别哪些钙化需要活检（减少不必要的活检）， 发现DCIS，以预测它是否会保持惰性或进展为致命的癌症（减少过度治疗）。