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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.
Abstract:
Recent advances in spatial omics technologies have enabled new approaches for analyzing tissue morphology, cell composition, and biomolecule expression patterns in situ. These advances are promoting the development of new computational tools and quantitative techniques in the emerging field of digital pathology. In this review, we survey current trends in the development of computational methods for spatially mapped omics data analysis using digitized histopathology slides and supplementary materials, with an emphasis on tools and applications relevant to genitourinary oncological research. The review contains three sections: 1) an overview of image processing approaches for histopathology slide analysis; 2) machine learning integration with spatially resolved omics data analysis; 3) a discussion of current limitations and future directions for integration of machine learning in the clinical decision-making process.
