Longitudinal deep neural networks for assessing metastatic brain cancer on a massive open benchmark.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".