ReviewBreast (Edinburgh, Scotland)2025
Computational pathology for breast cancer: Where do we stand for prognostic applications?
Review in Breast (Edinburgh, Scotland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Histology-Derived Signatures Predict Recurrence Risk and Chemotherapy Benefit in Randomized Trials of Early Breast Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Postmastectomy radiotherapy for early-stage breast cancer: can imaging bring us closer to personalized care?European radiology · 2026Article
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The very early days of artificial intelligence (AI) in healthcare are behind us. AI is now spreading in the healthcare sector and is gradually being implemented in routine clinical practice. Driven by the increasing digitization of microscope slides, computational pathology (CPath) is further strengthening the role of AI in the field of oncology. CPath is transforming fundamental research as well as routine clinical practice, both for diagnostic and prognostic applications. In breast cancer, CPath holds the potential to address several unmet clinical needs, particularly in the areas of biomarkers and prognostic tools. Indeed, multiple applications are on their way, ranging from predicting clinically meaningful endpoints to offering alternatives to gene-expression testing and detecting molecular alterations directly from digitized whole slide images. However, to fully harness the potential of CPath, several challenges must be overcome. These include improving the availability of multimodal patient data, advancing the digitalization of pathology laboratories, increasing adoption within the medical community, and navigating regulatory hurdles. This review offers an overview of the current landscape of CPath in breast cancer, highlighting the progress made and the hurdles that remain for its widespread clinical adoption in prognostic applications.
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Identifiers
What OpenQuestion holds
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.