ArticleNature communications2026
Subgroup performance of a commercial digital breast tomosynthesis model for breast cancer detection.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence-based multimodal integration of ultrasound and digital breast tomosynthesis for breast-level risk classification.Precision clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
AI models show potential to improve breast cancer screening, however detailed subgroup evaluations to uncover the strengths and weaknesses of models are lacking. This study presents a granular evaluation of a commercial AI model for cancer detection on digital breast tomosynthesis (DBT) on a retrospective cohort of 167,860 screening exams in female patients. Performance in distinguishing screen detected cancers (1,368 exams) from negative exams (166,387 exams) is stratified across demographic, imaging, and pathologic subgroups to identify disparities. The overall AUROC is 0.91 and sensitivity is 0.73 with robust performance across demographics. In-situ cancers (AUROC: 0.85, sensitivity: 0.55), calcifications (AUROC: 0.80, sensitivity: 0.66), and dense breast tissue (AUROC: 0.88, sensitivity: 0.63) are associated with lower performance, while masses (AUROC: 0.93, sensitivity: 0.85) and architectural distortions (AUROC: 0.90, sensitivity: 0.83) are associated with higher performance. These results highlight the need for detailed evaluations and vigilance in adopting new clinical tools.
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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.