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Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark

Author

Listed:
  • Katherine E. Link

    (NYU Langone Health
    NVIDIA)

  • Zane Schnurman

    (NYU Langone Health)

  • Chris Liu

    (NYU Langone Health
    NYU Tandon School of Engineering)

  • Young Joon (Fred) Kwon

    (NYU Langone Health)

  • Lavender Yao Jiang

    (NYU Langone Health
    New York University)

  • Mustafa Nasir-Moin

    (Harvard Medical School)

  • Sean Neifert

    (NYU Langone Health)

  • Juan Diego Alzate

    (NYU Langone Health)

  • Kenneth Bernstein

    (NYU Langone Health)

  • Tanxia Qu

    (NYU Langone Health)

  • Viola Chen

    (Eikon Therapeutics)

  • Eunice Yang

    (Columbia University Vagelos College of Surgeons and Physicians)

  • John G. Golfinos

    (NYU Langone Health)

  • Daniel Orringer

    (NYU Langone Health)

  • Douglas Kondziolka

    (NYU Langone Health)

  • Eric Karl Oermann

    (NYU Langone Health
    NYU Langone Health
    New York University)

Abstract

The detection and tracking of metastatic cancer over the lifetime of a patient remains a major challenge in clinical trials and real-world care. Advances in deep learning combined with massive datasets may enable the development of tools that can address this challenge. We present NYUMets-Brain, the world’s largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients. Using this dataset we developed Segmentation-Through-Time, a deep neural network which explicitly utilizes the longitudinal structure of the data and obtained state-of-the-art results at small (

Suggested Citation

  • Katherine E. Link & Zane Schnurman & Chris Liu & Young Joon (Fred) Kwon & Lavender Yao Jiang & Mustafa Nasir-Moin & Sean Neifert & Juan Diego Alzate & Kenneth Bernstein & Tanxia Qu & Viola Chen & Euni, 2024. "Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52414-2
    DOI: 10.1038/s41467-024-52414-2
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    References listed on IDEAS

    as
    1. Rebecca A. Burrell & Nicholas McGranahan & Jiri Bartek & Charles Swanton, 2013. "The causes and consequences of genetic heterogeneity in cancer evolution," Nature, Nature, vol. 501(7467), pages 338-345, September.
    2. Charles Swanton, 2020. "Take lessons from cancer evolution to the clinic," Nature, Nature, vol. 581(7809), pages 382-383, May.
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