MRI Radiomic Signatures of DCIS to Optimize Treatment

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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.