Predicting Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules Through Machine Learning and Radiomic Analysis of Clinical CT Data - Top-Institutions - MDSpire

Predicting Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules Through Machine Learning and Radiomic Analysis of Clinical CT Data

  • By

  • Mingzhi Lin

  • Longqian Li

  • Yiming Hui

  • Bin Li

  • Yue Li

  • ChongRui Li

  • Zhizhong Zheng

  • Zhuowen Yang

  • November 3, 2025

  • 0 min

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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

    • Thoracic Oncology
    • Radiology
    • Medical Imaging Informatics

  • #2

    MD Anderson Cancer Center
    MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Thoracic Oncology
    • Radiology
    • Computational Medicine

  • #3

    National Cancer Institute (NCI) - NIH
    National Cancer Institute (NCI) - NIH

    Bethesda, MD

    Key Differentiators

    • Cancer Imaging
    • Oncology Research
    • Machine Learning in Medicine

  • #4

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Thoracic Surgery
    • Biomedical Imaging

  • #5

    Fudan University Shanghai Cancer Center
    Fudan University Shanghai Cancer Center

    Shanghai, Shanghai

    Key Differentiators

    • Thoracic Oncology
    • Radiology
    • Medical AI

Original Source(s)

Predicting Invasiveness in Lung Adenocarcinoma with Ground-Glass Nodules Through Machine Learning and Radiomic Analysis of Clinical CT Data

  • by Mingzhi Lin, Longqian Li, Yiming Hui, Bin Li, Yue Li, ChongRui Li, Zhizhong Zheng, Zhuowen Yang

  • November 3, 2025

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