Estimation of histopathological types from breast MRI findings using a large language model - Top-Institutions - MDSpire

Estimation of histopathological types from breast MRI findings using a large language model

  • By

  • Rie Kanasaki

  • Kazufumi Suzuki

  • Takami Ota

  • Sadako Akashi-Tanaka

  • Yoji Nagashima

  • Shuji Sakai

  • April 14, 2026

  • 0 min

Share

Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Breast Imaging
    • Oncologic Pathology
    • Artificial Intelligence in Radiology

  • #2

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Breast Cancer Imaging
    • Medical AI Research

  • #3

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiology
    • Breast Imaging
    • Biomedical Informatics

  • #4

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Breast Radiology
    • Pathology
    • Medical AI

  • #5

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

    San Francisco, CA

    Key Differentiators

    • Breast Imaging
    • Pathology
    • AI in Medicine

Original Source(s)

Estimation of histopathological types from breast MRI findings using a large language model

  • by Rie Kanasaki, Kazufumi Suzuki, Takami Ota, Sadako Akashi-Tanaka, Yoji Nagashima, Shuji Sakai

  • April 14, 2026

Related Content

A patient with triple-negative breast cancer developed acute myocardial infarction after docetaxel chemotherapy: case report and literature review

Characteristics and risk factors of immune-related adverse events in patients receiving immune checkpoint inhibitor combination therapy

Percutaneous nephrostomy as a marker of clinical vulnerability in non-metastatic muscle-invasive bladder cancer: prognostic and infectious implications