ArticleThe American journal of surgical pathology2018
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.
Article in The American journal of surgical pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05689437 (MIRA Clinical Learning Environment), which is not on this map. Cited by 223 papers, 6 of them syntheses that pooled 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.
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.
MIRA Clinical Learning Environment (MIRACLE): Lung
Who cites it
223 citing papers in PubMed, 6 syntheses or guidelines pooled it, 467 citations in OpenAlex.
- Artificial Intelligence-Assisted Histopathologic Diagnosis and Grading of Oral Epithelial Dysplasia: A Systematic Review and Functional Meta-synthesis.Head and neck pathology · 2026Pooled it
- Artificial-Intelligence-Based Radiologic, Histopathologic, and Molecular Models for the Diagnosis and Classification of Malignant Salivary Gland Tumors: A Systematic Review and Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis.Scientific reports · 2023Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.JAMA network open · 2021Pooled it
- Comparative Analysis of Different Methods in the Quantification of Bone Matrix in Irradiated Rats.Journal of imaging informatics in medicine · 2026Article
- Ambient, real-time digitization and datafication of glass slide microscopy towards AI-at-the-microscope.Nature communications · 2026Article
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold.European journal of clinical investigation · 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
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Weakly supervised artificial intelligence for multi-cancer detection of lymph node metastasis on whole slide images.Nature communications · 2026Article
- Deep learning-assisted detection of lymph node metastases in bladder cancer.Diagnostic pathology · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Enhancing lymph node metastases assessment in breast cancer post-neoadjuvant therapy using artificial intelligence-driven diagnostics.Breast cancer research : BCR · 2026Article
- Stratifying amyloid burden in early Alzheimer's disease using cascaded attention-guided vision transformer using [¹⁸F]Florbetapir PET.European radiology · 2026Article
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
163 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology to improve histopathologic interpretation and clinical care. To evaluate the potential impact of digital assistance on interpretation of digitized slides, we conducted a multireader multicase study utilizing our deep learning algorithm for the detection of breast cancer metastasis in lymph nodes. Six pathologists reviewed 70 digitized slides from lymph node sections in 2 reader modes, unassisted and assisted, with a wash-out period between sessions. In the assisted mode, the deep learning algorithm was used to identify and outline regions with high likelihood of containing tumor. Algorithm-assisted pathologists demonstrated higher accuracy than either the algorithm or the pathologist alone. In particular, algorithm assistance significantly increased the sensitivity of detection for micrometastases (91% vs. 83%, P=0.02). In addition, average review time per image was significantly shorter with assistance than without assistance for both micrometastases (61 vs. 116 s, P=0.002) and negative images (111 vs. 137 s, P=0.018). Lastly, pathologists were asked to provide a numeric score regarding the difficulty of each image classification. On the basis of this score, pathologists considered the image review of micrometastases to be significantly easier when interpreted with assistance (P=0.0005). Utilizing a proof of concept assistant tool, this study demonstrates the potential of a deep learning algorithm to improve pathologist accuracy and efficiency in a digital pathology workflow.
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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.