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Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in
Hojung Kim1,2, Jina Kim1,3, Su Yeon Yeon2
1Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Frontiers in Oncology
|December 16, 2024
Summary
Spatial omics technologies transform digital pathology by enabling in situ analysis of tissue. This review highlights computational tools for genitourinary oncology, focusing on machine learning integration.
Area of Science:
- Computational pathology
- Spatial omics
- Genitourinary oncology
Background:
- Spatial omics technologies allow in situ analysis of tissue morphology, cell composition, and biomolecule expression.
- Advances drive computational tool development in digital pathology.
- Focus on genitourinary oncological research.
Purpose of the Study:
- Survey computational methods for spatially mapped omics data analysis.
- Emphasize tools and applications in genitourinary oncology.
- Discuss machine learning integration in clinical decision-making.
Main Methods:
- Review of image processing for histopathology slides.
- Analysis of machine learning integration with spatially resolved omics data.
- Discussion of current limitations and future directions.
Main Results:
- Identified trends in computational methods for spatial omics data.
- Highlighted machine learning applications in digital pathology.
- Outlined challenges and opportunities for clinical integration.
Conclusions:
- Computational tools are crucial for spatial omics data analysis in genitourinary oncology.
- Machine learning integration holds significant potential for clinical decision-making.
- Further research is needed to address current limitations and advance the field.
