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REVIEW article

Front. Oncol.
Sec. Genitourinary Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1465098
This article is part of the Research Topic The Role of AI in GU Oncology View all 5 articles

Machine Learning Approaches for Spatial Omics Data Analysis in Digital Pathology: Tools and Applications in Genitourinary Oncology

Provisionally accepted
Hojung Kim Hojung Kim 1Jina Kim Jina Kim 2Su Yeon Yeon Su Yeon Yeon 1Sungyong You Sungyong You 2*
  • 1 Department of Pathology, College of Medicine, University of Illinois at Chicago, Chicago, Illinois, United States
  • 2 Cedars Sinai Medical Center, Los Angeles, United States

The final, formatted version of the article will be published soon.

    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.

    Keywords: machine learning, spatial omics, digital pathology, Genitourinary, oncology

    Received: 15 Jul 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Kim, Kim, Yeon and You. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sungyong You, Cedars Sinai Medical Center, Los Angeles, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.