ArticleNPJ digital medicine2020
Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Diffusion attention expert model for predicting and semi-automatic localizing STAS in lung cancer histopathological images.Nature communications · 2026Article
- Interpretable multitask model for clinical pathology image prediction and interpretation.NPJ systems biology and applications · 2026Article
- Review
- Enhancing image retrieval through optimal barcode representation.Scientific reports · 2025Article
- Histology image analysis of 13 healthy tissues reveals molecular-histological correlations.Scientific reports · 2025Article
- Validation of histopathology foundation models through whole slide image retrieval.Scientific reports · 2025Article
- Using digital pathology to standardize and automate histological evaluations of environmental samples.Environmental toxicology and chemistry · 2025Review
- Explainable AI for classifying vertebral fracture histology in digital spine pathology.Frontiers in medicine · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- On image search in histopathology.Journal of pathology informatics · 2024Review
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.Nature medicine · 2024Article
- Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation.The American journal of pathology · 2024Article
- A self-supervised framework for cross-modal search in histopathology archives using scale harmonization.Scientific reports · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successfully indexed and searched almost 30,000 high-resolution digitized slides constituting 16 terabytes of data comprised of 20 million 1000 × 1000 pixels image patches. The TCGA image database covers 25 anatomic sites and contains 32 cancer subtypes. High-performance storage and GPU power were employed for experimentation. The results were assessed with conservative "majority voting" to build consensus for subtype diagnosis through vertical search and demonstrated high accuracy values for both frozen section slides (e.g., bladder urothelial carcinoma 93%, kidney renal clear cell carcinoma 97%, and ovarian serous cystadenocarcinoma 99%) and permanent histopathology slides (e.g., prostate adenocarcinoma 98%, skin cutaneous melanoma 99%, and thymoma 100%). The key finding of this validation study was that computational consensus appears to be possible for rendering diagnoses if a sufficiently large number of searchable cases are available for each cancer subtype.
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