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MRI Radiomic Signatures of DCIS to Optimize Treatment

DCIS 的 MRI 放射学特征可优化治疗

基本信息

批准号：
10655641
负责人：
Despina Kontos
金额：
$ 56.9万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
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/10655641
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 Classification Clinical Clinical Data Clinical Markers Collaborations Data Databases Detection Development Diagnosis Disease Eastern Cooperative Oncology Group Gene Expression Genomics Heterogeneity Histopathology Image In Situ Lesion Incidence Individual Institution Interobserver Variability Invaded 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 Prognosis Prognostic Factor Proliferating 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 Visualization Washington Woman Work aggressive therapy angiogenesis biomarker validation breast cancer diagnosis breast imaging calcification cancer invasiveness clinical database clinical diagnosis 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 tumor heterogeneity tumor microenvironment user-friendly

项目摘要

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放射学特征