ReviewInternational journal of molecular sciences2025
Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications.
Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Evaluating the Clinical Competence of Large Language Models in Prostate Cancer Management: A Comparative Study of DeepSeek-R1 and ChatGPT.Annals of surgical oncology · 2026Article
- Spatial proteomics: technical evolution and applications in cancer research.Frontiers in cell and developmental biology · 2026Review
- Perspective on the integration of radiomics and spatial omics in the analysis of the tumor microenvironment of bladder cancer and prospects for precision diagnosis and treatment.Frontiers in immunology · 2026Review
- Deciphering immunosuppressive niches by spatial single-cell proteomics: spatial interaction networks and translational opportunities.Frontiers in immunology · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 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
Artificial intelligence (AI) and its machine learning and deep learning algorithms have shown promise in oncological practice. Spatial information analysis in the context of cancer is crucial for its diagnosis and treatment because it can provide an understanding of tumor-microenvironment interactions and reveal insights into response to treatment. AI tools can analyze spatial information at multiple scales, highlighting key disease, clinical, and genetic phenotypes that may reveal underlying mechanisms and molecular markers of response and resistance within the tumor and its microenvironment. By examining tumor interactions at macroscopic (diagnostic imaging) and microscopic (pathology slides and spatial biology) levels, AI can assist in making important diagnostic and prognostic decisions. In this review, we first present an overview of AI and the need for multiscale spatial information in oncology. Then, we examine growing AI applications in the analysis of such information, focusing on diagnostic imaging, digital pathology, and spatial molecular biology. We also discuss applications of large-scale foundation models and task-oriented agentic AI in these fields as emergent technologies. Then, we discuss current limitations for the clinical translation of AI into regular utilization in cancer care and discovery.
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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.