ArticleNature communications2024
Longitudinal deep neural networks for assessing metastatic brain cancer on a large open benchmark.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Big data in U.S. neuro-oncology: trends and translational priorities.Journal of neuro-oncology · 2026Review
- An MRI Atlas of Regional Brain Vulnerability to Metastatic Disease.medRxiv : the preprint server for health sciences · 2026Article
- Temporal Dynamics and Heterogeneity in Brain Metastases: A Single-Center Retrospective Analysis of Vulnerabilities in Current MRI Surveillance Practices.Medicina (Kaunas, Lithuania) · 2026Observational
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Outcomes of concurrent versus non-concurrent immune checkpoint inhibition with stereotactic radiosurgery for melanoma brain metastases.Journal of neuro-oncology · 2025Article
- Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.Journal of magnetic resonance imaging : JMRI · 2025Article
- Article
- Improved Survival and Prognostication in Melanoma Patients With Brain Metastases: An Update of the Melanoma Graded Prognostic Assessment.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025Article
- Learning neuroimaging models from health system-scale data.Research square · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Article
- The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset.Radiology. Artificial intelligence · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
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 mm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.