ArticleNPJ precision oncology2025
Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- An interpretable weighted ensemble based on routinely collected clinical data for the accurate prediction of axillary lymph node metastasis.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
- Customized transformer for lymph node metastasis prediction from lung adenocarcinoma histology in a multicentric study.NPJ precision oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challenging and underexplored task for pathologists. We developed METACANS, an multimodal artificial intelligence (AI) model that integrates WSIs with clinicopathological features to predict ALN metastasis. METACANS was trained on 1991 cases and externally validated across five cohorts with a total of 2166 cases. Across all validation cohorts, METACANS achieved an area under the curve (AUC) of 0.733 (95% CI, 0.711-0.755), with an overall negative predictive value of 0.846, sensitivity of 0.820, specificity of 0.504, and balanced accuracy of 0.662. Without additional annotations, METACANS identified pathological imaging patterns linked to metastatic status, such as micropapillary growth, infiltrative patterns, and necrosis. While its predictive performance may not yet support immediate clinical application, METACANS addresses the task of predicting ALN metastasis using WSIs and clinicopathological features, and demonstrates the feasibility of multimodal AI approaches for preoperative axillary staging in breast cancer.
Identifiers
What Socratic holds
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 Socratic graph.