ReviewBiochimica et biophysica acta. Reviews on cancer2021
Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology.
Review in Biochimica et biophysica acta. Reviews on cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory - A Mini-review on the Benefits and Pitfalls of Its Implementation.Journal of clinical and translational pathology · 2025Article
- Immunotherapy in breast cancer: current landscape and emerging trends.Experimental hematology & oncology · 2025Review
- MLCDL: A Critical Practice and Implementation of Multi-tissue Classification and Diagnosis Using Deep Learning Algorithm.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Extended correlation functions for spatial analysis of multiplex imaging data.Biological imaging · 2024Article
- Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.Mayo Clinic proceedings. Digital health · 2023Review
- Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.Journal of cancer research and clinical oncology · 2023Review
- Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.Cancer reports (Hoboken, N.J.) · 2023Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Review
- Artificial intelligence in arthroplasty.Arthroplasty (London, England) · 2021Review
- Artificial Intelligence in Digital Pathology: What Is the Future?Healthcare (Basel, Switzerland) · 2021Article
- A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer.Cancers · 2021Article
- Artificial Intelligence in Digital Pathology: What Is the Future?Healthcare (Basel, Switzerland) · 2021Article
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
Abstract
The field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of artificial intelligence (AI) to important questions in immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use.
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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.