ReviewJournal of translational medicine2024
Artificial intelligence: illuminating the depths of the tumor microenvironment.
Review in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Artificial Intelligence as a Discovery Engine for Routine Molecular Techniques: Extracting Biological Insight from Western Blotting, ELISA, Immunostaining, and Immunoprecipitation.Cell biochemistry and biophysics · 2026Review
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Morpho-functional plasticity of tumour-associated macrophages: linking cell shape, ultrastructure, and immune regulation in cancer.Clinical and experimental medicine · 2026Review
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- Review
- Global trends in artificial intelligence applications for gastric cancer prediction, treatment, and management: a topic modeling and bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.BJC reports · 2026Review
- Next generation approaches in cancer immunotherapy targeting mechanisms beyond PD1 and PDL1.Discover oncology · 2026Review
- Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.NPJ precision oncology · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Advances in understanding the tumor microenvironment of neuroendocrine prostate cancer.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
- Spatial omics and its applications for a tumour microenvironment characterization and precision medicine approach of gastric cancer.Ecancermedicalscience · 2026Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation.Discover oncology · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry.Biomedical optics express · 2025Article
- Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.ACS pharmacology & translational science · 2025Article
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- AI-Driven Quality Monitoring and Control in Stem Cell Cultures: A Comprehensive Review.Biotechnology journal · 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
6 authors.
Funding
Abstract
Artificial intelligence (AI) can acquire characteristics that are not yet known to humans through extensive learning, enabling to handle large amounts of pathology image data. Divided into machine learning and deep learning, AI has the advantage of handling large amounts of data and processing image analysis, consequently it also has a great potential in accurately assessing tumour microenvironment (TME) models. With the complex composition of the TME, in-depth study of TME contributes to new ideas for treatment, assessment of patient response to postoperative therapy and prognostic prediction. This leads to a review of the development of AI's application in TME assessment in this study, provides an overview of AI techniques applied to medicine, delves into the application of AI in analysing the quantitative and spatial location characteristics of various cells (tumour cells, immune and non-immune cells) in the TME, reveals the predictive prognostic value of TME and provides new ideas for tumour therapy, highlights the great potential for clinical applications. In addition, a discussion of its limitations and encouraging future directions for its practical clinical application is presented.
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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.