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

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, Eunice Yang, John G. Golfinos, Daniel Orringer, Douglas Kondziolka and Eric Karl Oermann ()
Additional contact information
Katherine E. Link: NYU Langone Health
Zane Schnurman: NYU Langone Health
Chris Liu: NYU Langone Health
Young Joon (Fred) Kwon: NYU Langone Health
Lavender Yao Jiang: NYU Langone Health
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

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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 (

Date: 2024
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DOI: 10.1038/s41467-024-52414-2

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