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。乳腺癌筛查计划患有假乳房X线照片，不必要的活检， 过度诊断和过度治疗。筛查乳房X线摄影表现不佳的主要因素是 乳房X线检查钙化的诊断和预后不确定性。乳房钙化 代表从原位（DCIS）再到侵袭性癌症的从良性疾病到导管癌的生物连续体。 放射科医生努力将其成像外观与潜在的病理相关联和大约三分之二 活检钙化的良性病理恢复。尽管钙化会随着时间的流逝动态发展，但 当前的管理策略在很大程度上依赖于最新的钙化静态外观 乳房X线照片。大多数筛查计划中的女性都有多个乳房X线照片，但是这些时间信息是 在临床决策中始终未被充分利用。因此，迫切需要量化 来自连续乳房X线照片的钙化，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，预计越来越敏感的筛选方式 进一步增加了不必要的诊断检查和乳腺癌过度诊断的负担。中央 该提议的假设是动态可成像和组织生物标志物包含可起作的诊断和 有关乳房X线钙化的预后信息。使用已建立的诊断成像 （乳房X线摄影）与研究成像的生物标志物结合使用，可以对此假设进行检验。 该提案的关键将是创建大型回顾性和前瞻性收集的同类数据库 有连续乳房X线照片，组织样本和临床结局的患者。该提议将包括三个 具体目的：（1）开发乳腺钙化的静态模型，以改善 乳房X线摄影筛查； 2）开发乳腺钙化的动态模型，以预测组织病理学和 DCIS预后； 3）将动态钙化模型与基于组织的生物标志物相结合 描绘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线钙化分类的时间维度，产生关节 钙化生长轨迹和病变生物学的表征，并开发动态风险模型 预测正在进行DCI的主动监测的妇女的侵入性进展。该建议将由 Grimm博士（乳房放射科医生）和Ryser博士（数学建模者），由高度协作的支持 具有癌症生物学，计算机视觉和外科肿瘤学专业知识的多学科团队。总体 该提案的目的是开发一种动态的可成像生物标志物，该标志物描述了非非 - 通过利用连续乳房X线照片的时间维度来致命疾病。最终，长期目标 我们的工作是更好地确定活检的钙化（减少不必要的活检），以及侵入性的 发现DCI是为了预测它将保持懒惰还是对致命癌的进展（减少过度治疗）。