Diagnostic Imaging and Radiology
A Pragmatic Trial of Tomosynthesis vs Digital Mammography for Breast Cancer Screening (TMIST)
The Tomosynthesis Mammographic Imaging Screening Trial (TMIST or study EA1151) is a randomized breast cancer screening study. The ECOG-ACRIN Cancer Research Group opened the trial on July 6, 2017, and is currently enrolling about 165,000 women who are planning to have regular mammograms. TMIST is very important to the future of breast cancer screening because it will give us knowledge about how to move beyond our current “one size fits all” approach, where we screen most women the same way based on age-specific guidelines. The trial will help us move towards a more personalized approach that tailors mammography for each woman based on her own genetics and individual risk factors for developing breast cancer.
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Jon Steingrimsson
Associate Professor of Biostatistics, Director of the NextGen Graduate Program in Biostatistics -
Ani Eloyan
Vice Chair of the Department of Biostatistics, Associate Professor of Biostatistics
Advancing Quantitative Cancer Imaging in Clinical Trials (QIN)
The evolution in our understanding of cancer requires an evolution in the design and implementation of clinical trials, and the quantitative imaging used to assess therapeutic efficacy. This was noted in the Institute of Medicine's influential report A National Cancer Clinical Trials System for the 21st Century, which stated that 'the current structure and processes of the entire clinical trials system need to be redesigned to improve value by reducing redundancy and improving the effectiveness and efficiency of trials'. The goal of this study is to accelerate the development and deployment of quantitative imaging methods that improve the effectiveness and efficiency of clinical trials by using the combined resources of the American College of Radiology (ACR), the NCI-sponsored cooperative group ECOG-ACRIN (E-A), and the NCI Quantitative Imaging Network (QIN). To achieve this goal, and in accord with NOT-CA-13-011, this proposal is focused on supporting QIN network- wide research resources.
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Jon Steingrimsson
Associate Professor of Biostatistics, Director of the NextGen Graduate Program in Biostatistics -
Ani Eloyan
Vice Chair of the Department of Biostatistics, Associate Professor of Biostatistics
Analysis of Adverse Event Data to Optimize Strategies for the Longitudinal Assessment of Tolerability in the Context of Evolving Cancer Treatment Paradigms (EVOLV)
EVOLV proposes to deliver sophisticated and standardized methods for assessing, monitoring, analyzing, and reporting adverse events (AEs) experienced by individuals undergoing cancer treatment. These methods will harness the potential of the patient-reported outcomes version of the NCI Common Terminology Criteria for Adverse Events (PRO-CTCAETM) to provide previously unavailable patient perspectives on the tolerability of treatments (including targeted agents, immunotherapies, and other evolving treatments for which the type, severity, timing, and trajectory of adverse events is less known). Such information will help providers better identify and support patients at risk for treatment discontinuation, dose reductions, and treatment delays. Specifically, this study aims to: 1) perform longitudinal analyses of CTCAE and PRO-CTCAE data from trials conducted within the ECOG-ACRIN Cancer Research Group, using traditional and innovative strategies to examine AE trajectories and to produce a new reporting standard that reflects severity and fluctuations over time; 2) examine PRO-CTCAE and CTCAE predictors of treatment adherence and discontinuation; and 3) validate the broader predictive value of GP5, a single item from the Functional Assessment of Cancer TherapyGeneral (FACT-G) shown to predict early treatment discontinuation among women with breast cancer taking aromatase inhibitors. The study will also explore two novel measurement models for PRO-CTCAE scores and CTCAE grades: a phenotypic model including co-occurrence of symptoms and a cumulative burden index (CBI) for characterizing the quantity of burden accumulated by patients over time. Analyses will include demographic factors and insurance status to identify potential disparities.
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Jon Steingrimsson
Associate Professor of Biostatistics, Director of the NextGen Graduate Program in Biostatistics -
Ani Eloyan
Vice Chair of the Department of Biostatistics, Associate Professor of Biostatistics