Trial reportNature cancer2024
Clinical implementation of artificial-intelligence-assisted detection of breast cancer metastases in sentinel lymph nodes: the CONFIDENT-B single-center, non-randomized clinical trial.
Trial report in Nature cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 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
22 citing papers in PubMed.
- Deep learning in breast cancer histopathology: predicting and detecting axillary lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Review
- Guidance for laboratory implementation, governance and continuous assurance of artificial intelligence in histopathology.Virchows Archiv : an international journal of pathology · 2026Review
- Evaluating the Performance of LYDIA: An AI-Powered Assistant in the Detection of Metastatic Tumors to Optimize Clinical Workflows and Inform Soft Tissue Surgical Decision-Making.Bioengineering (Basel, Switzerland) · 2026Article
- From Static to Dynamic: Fluorescence Imaging Technology Advances Precise Embryo Evaluation.Small methods · 2026Review
- Weakly supervised artificial intelligence for multi-cancer detection of lymph node metastasis on whole slide images.Nature communications · 2026Article
- Artificial Intelligence in Early Breast Cancer Detection: A Systematic Review of Innovations in Preventive Women's Healthcare.Healthcare (Basel, Switzerland) · 2026Review
- The evolving physician-AI relationship: a five-tier framework for integrating intelligent systems into clinical practice and medical education.ESMO real world data and digital oncology · 2026Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Translational AI in whole-slide image cancer histopathology: state of the art and regulatory-approved solutions.Frontiers in digital health · 2026Review
- Raman and surface-enhanced Raman spectroscopy for intraoperative cancer diagnostics: sentinel lymph node biopsy, biomarkers and translational challenges.Frontiers in bioengineering and biotechnology · 2026Review
- Similarity-guided swarm of models: enhancing semi-supervised learning in computational pathology.Scientific reports · 2025Article
- Practical implementation of AI in a non-academic, non-commercial Pathology laboratory: Real world experience and lessons learned.Histopathology · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Advances in biomonitoring technologies for women's health.Nature communications · 2025Review
- Micrometastasis and Isolated Tumor Cells in Oral Squamous Cell Carcinoma: Refining Nodal Staging with Emerging Technologies.Head and neck pathology · 2025Review
- Preclinical models in the study of lymph node metastasis.Journal of Zhejiang University. Science. B · 2025Review
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Toward clinical applications of spatial-omics in cancer research.Nature cancer · 2024Article
- Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.Cancers · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathologists' assessment of sentinel lymph nodes (SNs) for breast cancer (BC) metastases is a treatment-guiding yet labor-intensive and costly task because of the performance of immunohistochemistry (IHC) in morphologically negative cases. This non-randomized, single-center clinical trial (International Standard Randomized Controlled Trial Number:14323711) assessed the efficacy of an artificial intelligence (AI)-assisted workflow for detecting BC metastases in SNs while maintaining diagnostic safety standards. From September 2022 to May 2023, 190 SN specimens were consecutively enrolled and allocated biweekly to the intervention arm (n = 100) or control arm (n = 90). In both arms, digital whole-slide images of hematoxylin-eosin sections of SN specimens were assessed by an expert pathologist, who was assisted by the 'Metastasis Detection' app (Visiopharm) in the intervention arm. Our primary endpoint showed a significantly reduced adjusted relative risk of IHC use (0.680, 95% confidence interval: 0.347-0.878) for AI-assisted pathologists, with subsequent cost savings of ~3,000 €. Secondary endpoints showed significant time reductions and up to 30% improved sensitivity for AI-assisted pathologists. This trial demonstrates the safety and potential for cost and time savings of AI assistance.
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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.