Nature Communications (Sep 2024)

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,
  • Eric Karl Oermann

DOI
https://doi.org/10.1038/s41467-024-52414-2
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 10

Abstract

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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 (<10 mm3) metastases detection and segmentation. We also demonstrate that the monthly rate of change of brain metastases over time are strongly predictive of overall survival (HR 1.27, 95%CI 1.18-1.38). We are releasing the dataset, codebase, and model weights for other cancer researchers to build upon these results and to serve as a public benchmark.