Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology
Year: 2024
Type: article
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.
Source: Frontiers in Oncology
Authors: Hojung Kim, Jina Kim, Su Yeon Yeon, Sungyong You
Institutions: Cedars-Sinai Medical Center, University of Illinois Chicago
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,295
Award: G3453033789
OpenAlex ID: W4404869031