Machine Learning and Artificial Intelligence–driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides

Hongming Xu, Fengyu Cong, Tae Hyun Hwang

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the tumor microenvironment, these approaches lack information related to spatial-level cellular distribution, co-organization, and cell-cell interaction in the TIME. With the emergence of computational pathology coupled with machine learning (ML) and artificial intelligence (AI), ML- and AI-driven spatial TIME analyses of pathology images could revolutionize our understanding of the highly heterogeneous and complex molecular architecture of the TIME. In this review we highlight recent studies on spatial TIME analysis of pathology slides using state-of-the-art ML and AI algorithms. Patient summary: This mini-review reports recent advances in machine learning and artificial intelligence for spatial analysis of the tumor immune microenvironment in pathology slides. This information can help in understanding the spatial heterogeneity and organization of cells in patient tumors.

Original languageEnglish (US)
Pages (from-to)706-709
Number of pages4
JournalEuropean Urology Focus
Volume7
Issue number4
DOIs
StatePublished - Jul 2021
Externally publishedYes

Keywords

  • Artificial intelligence
  • Machine learning
  • Molecular analysis
  • Pathology
  • Prognosis
  • Stratification
  • Tumor immune microenvironment

ASJC Scopus subject areas

  • Urology

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