Deep Learning-Based Automatic Detection of Brain Metastases in Heterogenous Multi-Institutional Magnetic Resonance Imaging Sets: An Exploratory Analysis of NRG-CC001.

Journal: International journal of radiation oncology, biology, physics
Published Date: Jul 2, 2022

Abstract

PURPOSE: Deep learning-based algorithms have been shown to be able to automatically detect and segment brain metastases (BMs) in magnetic resonance imaging, mostly based on single-institutional data sets. This work aimed to investigate the use of deep convolutional neural networks (DCNN) for BM detection and segmentation on a highly heterogeneous multi-institutional data set.

Authors

  • Ying Liang
    Department of Therapeutic Radiology, Yale University, New Haven, CT, U.S.A.
  • Karen Lee
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Joseph A Bovi
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Joshua D Palmer
    Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute at the Ohio State University, Columbus, Ohio.
  • Paul D Brown
    Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota.
  • Vinai Gondi
    Department of Radiation Oncology, Northwestern Medicine Cancer Center and Proton Center, Warrenville, Illinois.
  • Wolfgang A Tomé
  • Tammie L S Benzinger
    Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
  • Minesh P Mehta
    Miami Cancer Institute, Miami, Florida.
  • X Allen Li
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

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