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).

抽象的。乳腺癌筛查项目存在假阳性乳房X光检查、不必要的活检、 过度诊断和过度治疗。筛查乳腺 X 光检查性能不佳的一个主要原因是 乳房X光检查检测到的钙化的诊断和预后不确定性。乳房钙化 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影像表现与潜在的病理学联系起来，大约三分之二的人 的活检钙化灶以良性病理状态返回。尽管钙化随时间动态演变， 目前的管理策略在很大程度上依赖于最近的钙化的静态外观 乳房X光检查。大多数参与筛查计划的女性都会进行多次乳房 X 光检查，但这种时间信息是 在临床决策中始终未得到充分利用。因此，迫切需要量化其动态 连续乳房 X 光检查中的钙化，并表征钙化轨迹之间的关系 和疾病生物学。在缺乏此类创新的情况下，预计筛查方式将变得越来越敏感 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 该提案的假设是动态可成像和组织生物标志物包含可操作的诊断和 有关乳房X线照相钙化的预后信息。使用已建立的诊断成像 （乳房X线照相术）与研究性可成像生物标志物相结合将能够检验这一假设。 该提案的关键是创建一个包含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光检查、组织样本和临床结果。该提案将包括三部分 具体目标：（1）开发乳腺钙化的静态模型，以改善乳腺钙化的临床表现 乳房X光检查； 2) 开发乳腺钙化的动态模型来预测组织病理学和 DCIS 预后； 3) 将动态钙化模型与基础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 乳房X线照相钙化的计算机辅助分类的时间维度产生了联合 钙化生长轨迹和病变生物学的表征，并开发动态风险模型 预测接受 DCIS 主动监测的女性的侵袭性进展。该提案将由以下人士共同牵头 Grimm 博士（乳腺放射科医生）和 Ryser 博士（数学建模师）得到高度协作的支持 多学科团队拥有癌症生物学、计算机视觉和肿瘤外科方面的专业知识。整体 该提案的目标是开发一种动态可成像生物标志物，将致命性癌症与非致命性癌症区分开来。 利用连续乳房X光检查的时间维度来发现致命疾病。最终的长期目标是 我们的工作是更好地确定哪些钙化需要活检（减少不必要的活检），以及是否需要进行侵入性检查 发现 DCIS 可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）。