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Nova · Professor Researcher · re-ranking top 20…
Emily Fox Conant

Emily Fox Conant

Verified

University of Pennsylvania · Rehabilitation Medicine

Active 1988–2024

h-index70
Citations19.7k
Papers33388 last 5y
Funding$19.9M
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Research topics

  • Internal medicine
  • Medicine
  • Oncology
  • Radiology
  • Pathology
  • Family medicine
  • Gynecology
  • Medical physics

Selected publications

  • NCCN Guidelines® Insights: Breast Cancer Screening and Diagnosis, Version 1.2023

    Journal of the National Comprehensive Cancer Network · 2023 · 134 citations

    • Medicine
    • Family medicine
    • Medical physics

    The NCCN Guidelines for Breast Cancer Screening and Diagnosis provide health care providers with a practical, consistent framework for screening and evaluating a spectrum of clinical presentations and breast lesions. The NCCN Breast Cancer Screening and Diagnosis Panel is composed of a multidisciplinary team of experts in the field, including representation from medical oncology, gynecologic oncology, surgical oncology, internal medicine, family practice, preventive medicine, pathology, diagnostic and interventional radiology, as well as patient advocacy. The NCCN Breast Cancer Screening and Diagnosis Panel meets at least annually to review emerging data and comments from reviewers within their institutions to guide updates to existing recommendations. These NCCN Guidelines Insights summarize the panel's decision-making and discussion surrounding the most recent updates to the guideline's screening recommendations.

  • Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities

    Radiology · 2023 · 76 citations

    • Medicine
    • Radiology
    • Oncology

    Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

  • Five Consecutive Years of Screening with Digital Breast Tomosynthesis: Outcomes by Screening Year and Round

    Radiology · 2020 · 105 citations

    1st authorCorresponding
    • Medicine
    • Internal medicine

    See also the editorial by Moy and Heller in this issue.

Recent grants

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