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Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications
Ali A Tarhini1, Issam El Naqa1
1Moffitt Cancer Center, Tampa, FL 33612, USA.
International Journal of Molecular Sciences
|August 28, 2025
Summary
Artificial intelligence (AI) enhances cancer care by analyzing spatial data from imaging to molecular levels. This approach aids diagnosis, treatment response prediction, and understanding tumor-microenvironment interactions for better outcomes.
Area of Science:
- Oncology
- Artificial Intelligence
- Bioinformatics
Background:
- Spatial information is vital for understanding tumor-microenvironment interactions in oncology.
- Artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer powerful tools for analyzing complex biological data.
- Multiscale spatial data analysis is crucial for cancer diagnosis, treatment response prediction, and uncovering resistance mechanisms.
Purpose of the Study:
- To review the current applications of AI in analyzing multiscale spatial information in oncology.
- To explore the potential of emergent AI technologies like foundation models and agentic AI in cancer research.
- To discuss the challenges and limitations hindering the clinical translation of AI in cancer care.
Main Methods:
- Review of existing literature on AI applications in oncological spatial data analysis.
- Examination of AI"s role in diagnostic imaging, digital pathology, and spatial molecular biology.
- Discussion of advanced AI techniques, including foundation models and agentic AI.
Main Results:
- AI tools effectively analyze spatial information across macroscopic and microscopic scales.
- AI highlights key phenotypes and molecular markers influencing treatment response and resistance.
- Emerging AI technologies show promise for deeper insights into cancer biology.
Conclusions:
- AI holds significant potential to advance cancer diagnosis, prognosis, and treatment strategies.
- Multiscale spatial analysis powered by AI can reveal critical insights into tumor biology and patient outcomes.
- Addressing current limitations is essential for integrating AI into routine clinical oncology practice.
Keywords:
AI agentsartificial intelligencedeep learningfoundation modelsmachine learningmultiscale spatial informationpathomicsradiomicsspatial proteomicsspatial transcriptomicsMore Related Videos
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