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

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 pr

SUBMITTER: Link KE 

PROVIDER: S-EPMC11408643 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature