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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
AI agentsartificial intelligencedeep learningfoundation modelsmachine learningmultiscale spatial informationpathomicsradiomicsspatial proteomicsspatial transcriptomics

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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.