MRI Radiomic Signatures of DCIS to Optimize Treatment

DCIS 的 MRI 放射学特征可优化治疗

• 批准号: 10537149
• 负责人: Despina Kontos
• 金额: $ 59.75万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10537149
• 关键词: Address; Affect; Aftercare; American College of Radiology Imaging Network; Anxiety; Biological; Biological Assay; Biology; Breast; Breast Cancer Detection; Breast Magnetic Resonance Imaging; Clinical; Clinical Data; Clinical Markers; Collaborations; Data; Databases; Detection; Development; Diagnosis; Disease; Eastern Cooperative Oncology Group; Gene Expression; Genomics; Gold; Heterogeneity; Histopathology; Image; In Situ Lesion; Incidence; Individual; Institution; Interobserver Variability; Ipsilateral; Link; Local Therapy; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Mammographic screening; Mammography; Measures; Medical; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Newly Diagnosed; Noise; Noninfiltrating Intraductal Carcinoma; Normal tissue morphology; Oncology; Operative Surgical Procedures; Outcome; Pathologic; Pathology; Patients; Penetration; Pennsylvania; Performance; Phenotype; Physicians; Plant Roots; Prognosis; Prognostic Factor; Public Health; Radiation therapy; Radiosurgery; Recurrence; Reproducibility; Risk; Risk Assessment; Sampling; Science; Semantics; Signal Transduction; Staging; Standardization; Statistical Data Interpretation; Survival Rate; Systemic Therapy; Testing; Thick; Tissue Sample; Tissues; Universities; Unnecessary Surgery; Washington; Woman; Work; aggressive therapy; angiogenesis; base; breast cancer diagnosis; breast imaging; calcification; cancer invasiveness; clinical database; clinical prognostic; cohort; combat; experience; health goals; high risk; hormone therapy; imaging biomarker; improved; indexing; inter-institutional; malignant breast neoplasm; molecular marker; multidimensional data; non-invasive imaging; novel; novel strategies; oncotype; open source; overtreatment; phenomics; phenotypic data; prognostic; prognostic index; prognostic model; radiomics; risk prediction model; risk stratification; side effect; software development; standard of care; statistical center; tool; treatment optimization; tumor; user-friendly

Abstract/Project Summary: The purpose of this study is to determine whether breast MRI radiomic features can be utilized to optimize treatment of ductal carcinoma in situ (DCIS), the earliest form of breast cancer diagnosed. Although DCIS survival rates approach 100%, there is concern that its management generally results in overtreatment, exposing many of the 50,000 U.S. women diagnosed each year to unnecessary anxiety and morbidity. The vast majority of DCIS is detected in asymptomatic women in whom suspicious calcifications are identified on mammography and characterized using limited tissue histopathology. Unfortunately, conventional imaging and pathology have not proven reliable for distinguishing low vs. high-risk DCIS. Specifically, it is unclear at diagnosis which forms of DCIS will upstage to invasive disease or have an ipsilateral breast recurrence (IBR) after treatment. This limited risk-stratification is due in part to inadequate sampling of the entire DCIS lesion and an inability to account for peritumoral microenvironment features. This results in unnecessary surgery, radiation therapy, and medical therapy for as many as half of women diagnosed with DCIS. Breast MRI is commonly and easily performed, able to best depict DCIS span, and can assess tumor and peritumoral heterogeneity rooted in biological features such as angiogenesis, making it an appealing choice for a radiomics assay to improve DCIS risk assessments. The Quantitative Breast Imaging Lab at the University of Washington has shown that quantitative MRI features are associated with DCIS grade, a molecular marker of recurrence (Oncotype DX DCIS Score), and IBR. The Computational Biomarker Imaging Group at the University of Pennsylvania has pioneered breast MRI radiomic phenotyping and shown radiomic measures of breast cancers correlate with genomic features and recurrence. The Center for Statistical Sciences at Brown University has expertise with radiomics, machine learning, and statistical analyses for imaging trials from ECOG-ACRIN. In this collaborative application, we hypothesize that breast MRI radiomic signatures of DCIS will result in distinct phenotypes that are prognostic and can be integrated with clinical, molecular, and pathologic markers to optimize DCIS treatment. To test this hypothesis, we will create a multi-institutional database of over 1400 MRIs, including exams from the ECOG-ACRIN E4112 trial, with curated outcomes (e.g., upstage to invasion, DCIS Score, and IBR). Leveraging a novel approach to harmonize multicenter data (nested-Combat radiomic feature standardization), we will discover and validate MRI radiomic phenotypes and assess those phenotypes’ associations with invasive upstaging, Oncotype DX DCIS Score, and 5- and 10-year IBR. Finally, we will determine whether integration of these phenotypes into existing clinical prognostic indices (e.g., Van Nuys Prognostic Index) can provide more precise estimates of IBR. If successful, this study will help clinicians de-escalate DCIS therapy in low-risk patients and address an important public health goal: decreasing breast cancer overtreatment.

摘要/项目摘要：本研究的目的是确定乳腺MRI的放射学特征 可用于优化导管原位癌(DCIS)的治疗，DCIS是乳腺癌的最早形式 诊断出来了。尽管DCIS的存活率接近100%，但人们担心其管理通常 导致过度治疗，使每年确诊的50,000名美国女性中的许多人面临不必要的风险 焦虑和病态。绝大多数DCIS是在无症状、可疑的妇女中发现的 钙化是在乳房X光检查上发现的，并通过有限的组织病理学来表征。 不幸的是，传统的影像和病理学在区分低风险和高风险方面并不可靠。 毒品调查科。具体地说，在诊断时还不清楚哪些形式的DCIS会抢在侵袭性疾病的舞台上，或者有 治疗后同侧乳房复发(IBR)。这种有限的风险分层在一定程度上是由于不充分 对整个DCIS病变进行采样，无法解释瘤周微环境特征。这 导致多达一半的女性接受不必要的手术、放射治疗和药物治疗 被诊断为DCIS。乳腺MRI是一种常见且简单的检查方法，能够很好地显示DCIS的跨度，并且可以 评估源于血管生成等生物学特征的肿瘤和瘤周异质性，使其成为 改善DCIS风险评估的放射组学分析是一个有吸引力的选择。乳房的定量成像 华盛顿大学的实验室表明，定量核磁共振特征与DCIS分级有关， 复发的分子标志物(肿瘤型DX DCIS评分)和IBR。计算生物标记物成像 宾夕法尼亚大学的一个研究小组开创了乳腺MRI放射表型的先河，并展示了放射 乳腺癌的衡量标准与基因组特征和复发相关。统计中心 布朗大学的科学拥有放射组学、机器学习和统计分析方面的专业知识 来自ECOG-ACRIN的成像试验。在这个协作应用程序中，我们假设乳腺MRI放射学 DCIS的信号将导致不同的表型，这些表型可以预测预后，并可以与 临床、分子和病理标记物，以优化DCIS治疗。为了检验这一假设，我们将 建立一个包含1400多项核磁共振检查的多机构数据库，包括ECOG-ACRIN E4112试验的检查， 有精心策划的结果(例如，进入侵袭阶段、DCIS评分和IBR)。利用一种新方法 协调多中心数据(嵌套战斗放射特征标准化)，我们将发现和验证 MRI放射表型及评估这些表型与侵袭性肿瘤DX分期的关系 DCIS评分，以及5年和10年IBR。最后，我们将确定这些表型是否整合到 现有的临床预后指数(例如Van Nuys预后指数)可以提供更准确的估计 IBR.如果成功，这项研究将帮助临床医生降低低风险患者的DCIS治疗，并解决 重要的公共卫生目标：减少乳腺癌的过度治疗。