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Constance D. Lehman

Constance D. Lehman

D-Index & Metrics

Medicine

D-Index
92
Citations
31192
World Ranking
11304
National Ranking
5801

Overview

Constance D. Lehman is affiliated with Harvard University in the United States and has established a significant presence in medical research, particularly within the fields of radiology, oncology, and cancer research. Their work encompasses a broad range of topics focused on cancer detection, risk factors, and imaging technologies.

Their research prominently covers global cancer incidence and screening, radiomics and machine learning in medical imaging, AI applications in cancer detection, breast cancer treatment studies, MRI in cancer diagnosis, digital radiography and breast imaging, and cancer risks and factors.

Frequently collaborating with other researchers, Lehman has worked alongside Leslie R. Lamb, Brian N. Dontchos, Sarah Mercaldo, Randy C. Miles, and Anand K. Narayan. These partnerships contribute to a body of research published across multiple specialized venues.

Lehman's publications appear regularly in well-known academic journals. The most frequent publication venues include UNC Libraries, Journal of Clinical Oncology, Journal of the American College of Radiology, Radiology, and Cancer Research.

Key recent papers by Lehman and collaborators include:

  • Toward robust mammography-based models for breast cancer risk, 2021, Science Translational Medicine
  • Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model, 2021, Journal of Clinical Oncology
  • Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis, 2023, Radiology
  • Relationship of established risk factors with breast cancer subtypes, 2021, Cancer Medicine
  • Unilateral Lymphadenopathy After COVID-19 Vaccination: A Practical Management Plan for Radiologists Across Specialties, 2021, Journal of the American College of Radiology

Lehman's work spans numerous subfields of study within medicine, including radiology, nuclear medicine and imaging, oncology, cancer research, artificial intelligence, and pulmonary and respiratory medicine. The integration of AI and machine learning techniques is a notable aspect of their research, especially in the context of improving models for breast cancer risk prediction and detection technologies.

This extensive research portfolio underlines Lehman's engagement with advancing cancer diagnostics and treatment through interdisciplinary approaches, involving imaging innovations and computational methods.

Best Publications

  • American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography

    Debbie Saslow;Carla Boetes;Wylie Burke;Steven Harms

  • Screening for Breast Cancer

    Joann G. Elmore;Katrina Armstrong;Constance D. Lehman;Suzanne W. Fletcher

  • MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer

    Constance D. Lehman;Constantine Gatsonis;Christiane K. Kuhl;R. Edward Hendrick

  • Diagnostic architectural and dynamic features at breast MR imaging: Multicenter study

    Mitchell D. Schnall;Jeffrey Blume;David A. Bluemke;Gia A. DeAngelis

  • Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection

    Constance D. Lehman;Robert D. Wellman;Diana S. M. Buist;Karla Kerlikowske

  • Magnetic Resonance Imaging of the Breast Prior to Biopsy

    David A. Bluemke;Constantine A. Gatsonis;Mei Hsiu Chen;Gia A. DeAngelis

  • A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction

    Adam Yala;Constance Lehman;Tal Schuster;Tally Portnoi

  • National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium

    Constance D. Lehman;Robert F. Arao;Brian L. Sprague;Janie M. Lee

  • Screening women at high risk for breast cancer with mammography and magnetic resonance imaging.

    Constance D. Lehman;Jeffrey D. Blume;Paul Weatherall;David Thickman

  • Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY TRIAL

    Nola M. Hylton;Jeffrey D. Blume;Wanda K. Bernreuter;Etta D. Pisano

  • Cancer yield of mammography, MR, and US in high-risk women: prospective multi-institution breast cancer screening study.

    Constance D. Lehman;Claudine Isaacs;Mitchell D. Schnall;Etta D. Pisano

  • Pathologic Complete Response Predicts Recurrence-Free Survival More Effectively by Cancer Subset: Results From the I-SPY 1 TRIAL—CALGB 150007/150012, ACRIN 6657

    Laura J. Esserman;Donald A. Berry;Angela DeMichele;Lisa Carey

  • Breast Cancer Screening and Diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology.

    Therese B. Bevers;Mark Helvie;Ermelinda Bonaccio;Kristine E. Calhoun

  • Performance benchmarks for screening mammography.

    Robert D. Rosenberg;Bonnie C. Yankaskas;Linn A. Abraham;Edward A. Sickles

  • Chemotherapy response and recurrence-free survival in Neoadjuvant breast cancer depends on biomarker profiles: Results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657)

    Laura J. Esserman;Donald A. Berry;Maggie C. U. Cheang;Christina Yau

  • Factors contributing to mammography failure in women aged 40-49 years

    Diana S. M. Buist;Peggy L. Porter;Constance Lehman;Stephen H. Taplin

  • Quantitative Diffusion-Weighted Imaging as an Adjunct to Conventional Breast MRI for Improved Positive Predictive Value

    Savannah C. Partridge;Wendy B. DeMartini;Brenda F. Kurland;Peter R. Eby

  • Comparative Effectiveness of Digital Versus Film-Screen Mammography in Community Practice in the United States: A Cohort Study

    Karla Kerlikowske;Rebecca A. Hubbard;Diana L. Miglioretti;Berta M. Geller

  • ACR Appropriateness Criteria Breast Cancer Screening

    Martha B. Mainiero;Ana Lourenco;Mary C. Mahoney;Mary S. Newell

  • Diagnostic Architectural and Dynamic Features at Breast MR

    Mitchell D. Schnall;Jeffrey Blume;David A. Bluemke;Gia A. DeAngelis

Frequent Co-Authors

Mitchell D. Schnall
Mitchell D. Schnall University of Pennsylvania
Karla Kerlikowske
Karla Kerlikowske University of California, San Francisco
Diana L. Miglioretti
Diana L. Miglioretti University of California, Davis
Constantine Gatsonis
Constantine Gatsonis Brown University
Elizabeth A. Morris
Elizabeth A. Morris Memorial Sloan Kettering Cancer Center
Christiane K. Kuhl
Christiane K. Kuhl RWTH Aachen University
David A. Mankoff
David A. Mankoff University of Pennsylvania
Laura J. Esserman
Laura J. Esserman University of California, San Francisco
Mark A. Rosen
Mark A. Rosen University of Pennsylvania

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