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

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...

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Bibliographic Details
Published in:Nature communications 2024-01, Vol.15 (1), p.8170
Main Authors: Link, Katherine E, Schnurman, Zane, Liu, Chris, Kwon, Young Joon (Fred), Jiang, Lavender Yao, Nasir-Moin, Mustafa, Neifert, Sean, Alzate, Juan Diego, Bernstein, Kenneth, Qu, Tanxia, Chen, Viola, Yang, Eunice, Golfinos, John G, Orringer, Daniel, Kondziolka, Douglas, Oermann, Eric Karl
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Benchmarks
Biomarkers
Brain
Brain tumors
Cancer
Clinical trials
Datasets
Deep learning
Disease control
Image segmentation
Machine learning
Massive data points
Medical research
Metastases
Metastasis
Neural networks
Neuroimaging
Patients
Predictive control
Segmentation
Survival
Time dependence
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Summary: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 (
ISSN:2041-1723
DOI:10.1038/s41467-024-52414-2