ReviewESMO real world data and digital oncology2026
How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.
Review in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Variation in chemotherapy use across healthcare regions in Sweden and its impact on prognosis in women with HR+/HER2- early breast cancer.Breast cancer research : BCR · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The global increase in breast cancer incidence is determining substantial augment of pathologists' workload. Digital pathology (DP), which consists of digitizing haematoxylin-eosin (H&E)-stained slides into whole-slide images, enables new workflows and horizons. The integration of artificial intelligence (AI) with DP led to the emergence of computational pathology (CP), which has the potential of offering valuable diagnostic, prognostic and predictive tools. The aim of this narrative review is to provide an overview of the state of the art in this rapidly evolving field. Diagnostic predictive and prognostic application: Several AI tools demonstrated enhanced performances as compared with visual evaluation of pathologists, such as identification of invasive breast cancers, sentinel lymph node metastasis, histological grading, Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2) and programmed death-ligand 1 (PD-L1) quantification. Due to this, most of them are currently authorized in the European market. Beyond diagnosis, CP has shown strong potential in predicting, from a simple H&E digital slide, treatment response (e.g. neoadjuvant chemotherapy, anti-HER2 therapy and endocrine therapy), immunohistochemistry status (ER, PR, HER2, PD-L1), molecular alterations (e.g. Conclusion: Despite challenges such as high validation costs, initial economic investments and potential AI biases, CP holds great promise. Continued effort to address these barriers and improve AI reliability are crucial. Emerging AI tools, including agent-driven systems, point towards the potential integration of CP into routine clinical workflow practice, even though clinical validation has not yet been established.
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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.