ReviewThe Journal of pathology2023
Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.
Review in The Journal of pathology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for the adoption of digital pathology in clinical pathology units recommended by the polish society of pathologists.Diagnostic pathology · 2026Guideline
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- NS-GUSL: Green U-Shaped Learning for Nuclei Segmentation from Histopathology Images.Journal of imaging · 2026Article
- [Colorectal cancer diagnosis method based on dynamic gland-aware and tissue soft-clustering].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Optimization of Ki67 digital image analysis in breast cancer by automated tumor area identification.Virchows Archiv : an international journal of pathology · 2026Article
- Computational pathology-based artificial intelligence platform for the identification of common oral potentially malignant disorders.BMC oral health · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Debating the pros and cons of computational pathology at the European Congress of Pathology (ECP) 2024.Virchows Archiv : an international journal of pathology · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
- Validation of fibroblast activation protein and α-smooth muscle actin as prognostic biomarkers in prostate cancer through AI-assisted image analysis of dual-marker IHC.The journal of pathology. Clinical research · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.Clinical and experimental dental research · 2025Article
- A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images.Diagnostics (Basel, Switzerland) · 2025Article
- Prognostic prediction in soft-tissue sarcomas using deep learning and digital pathology of tumor and margin areas.Scientific reports · 2025Article
- An open-source platform for structured annotation and computational workflows in digital pathology research.Scientific reports · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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.