Evidence map›Paper›PMID 41773279›Full record

Article... International Conference on Learning Representations2025

Time-to-Event Pretraining for 3D Medical Imaging.

Zepeng Huo, Jason Alan Fries, Alejandro Lozano, Jeya Maria Jose Valanarasu, Ethan Steinberg, Louis Blankemeier, Akshay S Chaudhari, Curtis Langlotz, Nigam H Shah

Abstract read
In one paragraph

Article in ... International Conference on Learning Representations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Anatomically-guided masked autoencoder pre-training for aneurysm detection.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zepeng HuoCenter for Biomedical Informatics Research, Stanford University.
Jason Alan FriesCenter for Biomedical Informatics Research, Stanford University.
Alejandro LozanoDepartment of Biomedical Data Science, Stanford University.
Jeya Maria Jose ValanarasuDepartment of Computer Science, Stanford University.
Ethan SteinbergCenter for Biomedical Informatics Research, Stanford University.
Louis BlankemeierDepartment of Biomedical Data Science, Stanford University.
Akshay S ChaudhariDepartment of Biomedical Data Science, Stanford University.
Curtis LanglotzStanford Medical Center, Stanford Health Care.
Nigam H ShahStanford Medical Center, Stanford Health Care.

Funding

TR&D Project 3: OpenSim for PredictionP41EB027060 · NIBIB · STANFORD UNIVERSITY · PI Joy P Ku · 2020 to 2026
$9.7M
RADX TECH - CERES NANOSCIENCE INC NANOTRAP PARTICLE MANUFACTURING75N92020C00021 · NHLBI · CERES NANOSCIENCES, LLLP · PI DUNLAP, ROSS · 2021 to 2021
$4.0M
Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep LearningR01HL167974 · NHLBI · STANFORD UNIVERSITY · PI Imon Banerjee, Akshay Chaudhari · 2023 to 2026
$2.4M
Novel Incidental Calcium Evaluation (NICE)R01HL169345 · NHLBI · STANFORD UNIVERSITY · PI Imon Banerjee, Akshay Chaudhari · 2024 to 2026
$2.1M
Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center StudyR01HL155410 · NHLBI · STANFORD UNIVERSITY · PI LANGLOTZ, CURTIS P, SHAH, NIGAM H · 2021 to 2024
$2.1M
NHLBI NIH HHS 75N92020C00008NHLBI NIH HHS 75N92020C00021NHLBI NIH HHS R01 HL155410NHLBI NIH HHS R01 HL167974NHLBI NIH HHS R01 HL169345NIBIB NIH HHS P41 EB027060
6 · The paper itself

Abstract

With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for 3D medical imaging models capture local structural features like organ morphology, they fail to link pixel biomarkers with long-term health outcomes due to a

Identifiers

PMID41773279
PMCPMC12950328

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.