Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial - Top-Institutions - MDSpire

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial

  • By

  • Markus H. A. Janse

  • Liselore M. Janssen

  • Elian J. M. Wolters-van der Ben

  • Maaike R. Moman

  • Max A. Viergever

  • Paul J. van Diest

  • Kenneth G. A. Gilhuijs

  • August 6, 2025

  • 0 min

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Top Institutions in Radiology

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Breast Oncology
    • Radiology
    • Medical Imaging Informatics
    • Machine Learning in Oncology

  • #2

    Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute

    Boston, MA

    Key Differentiators

    • Breast Medical Oncology
    • Radiology
    • Computational Oncology

  • #3

    MD Anderson Cancer Center
    MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Breast Surgical Oncology
    • Radiology
    • Biomedical Engineering

  • #4

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Oncology
    • Artificial Intelligence in Medicine

  • #5

    University of California, San Francisco (UCSF) Medical Center
    University of California, San Francisco (UCSF) Medical Center

    San Francisco, CA

    Key Differentiators

    • Breast Imaging
    • Medical Oncology
    • Biomedical Informatics

Original Source(s)

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial

  • by Markus H. A. Janse, Liselore M. Janssen, Elian J. M. Wolters-van der Ben, Maaike R. Moman, Max A. Viergever, Paul J. van Diest, Kenneth G. A. Gilhuijs

  • August 6, 2025

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