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

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. 1–3 Recent advances in deep learning combined with massive, real-world datasets may enable the development of tools that can address this challenge. We present our work with the NYUMets Project to develop NYUMets-Brain and a novel longitudinal deep neural network (DNN), segmentation-through-time (STT). NYUMets-Brain is the world's largest, longitudinal, real-world dataset of cancer consisting of the imaging, clinical follow-up, and medical management of 1,429 patients with an average of six MRI studies obtained over 17 months per patient. The dataset includes over 2,367 expert segmentations of metastatic brain tumors, and 81,562 medical prescriptions. Using this dataset we developed Segmentation Through Time (STT), a deep neural network (DNN) which explicitly utilizes the longitudinal structure of the data and obtained state of the art results at tumor segmentation and detection of small (< 10 mm3) metastases. We also demonstrate that longitudinal measurements to assess 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 entire dataset, codebase, trained model weights, and an interface for dataset access for other cancer researchers to build upon these results and to serve as a public benchmark. Massive real-world datasets and public benchmarks such as NYUMets-Brain may enable the tracking and detection of metastatic brain cancer, and be broadly applicable to advancing the development of AI models in other types of metastatic cancer as well.