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Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy

Pingjun Chen1, Jianjun Zhang2,3, Jia Wu1,2

  • 1Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

21St Century Pathology
|October 25, 2022
PubMed

Insights

Artificial intelligence (AI) in digital pathology enhances immune-checkpoint inhibitor (ICI) therapy by analyzing multiplex bioimaging data. This approach aims to discover novel biomarkers for predicting patient response to immunotherapy, improving cancer treatment selection.

Area of Science:

  • Oncology
  • Immunotherapy
  • Digital Pathology
  • Artificial Intelligence

Background:

  • Immune-checkpoint inhibitors (ICIs) have transformed cancer treatment but benefit only 20-30% of lung cancer patients.
  • Current predictive biomarkers (histology, PD-L1, TMB) lack exclusivity, necessitating improved methods for patient selection.
  • The tumor microenvironment's complexity requires advanced analytical tools for comprehensive characterization.

Purpose of the Study:

  • To explore the role of artificial intelligence (AI) and digital pathology in advancing immunotherapy biomarkers.
  • To summarize recent AI applications in analyzing tissue slides for cancer treatment.
  • To identify emerging paradigms for developing more effective predictive biomarkers for ICI therapy.

Main Methods:

  • Leveraging AI, particularly deep learning, for automated analysis of digital pathology slides.
  • Utilizing multiplex bioimaging technology to comprehensively characterize the tumor microenvironment.
  • Integrating histopathologic and molecular data with AI-driven image analysis.

Main Results:

  • AI-powered digital pathology enables detailed analysis of immune cell distribution, function, and interactions within the tumor microenvironment.
  • Multiplex bioimaging provides a deeper understanding of the complex cellular landscape relevant to immunotherapy response.
  • Emerging AI studies demonstrate potential for automated biomarker discovery from tissue slide data.

Conclusions:

  • AI in digital pathology offers a powerful approach to overcome limitations of current predictive biomarkers for ICIs.
  • Advanced imaging and AI integration are crucial for developing novel, effective biomarkers to guide immunotherapy selection.
  • Future research directions focus on refining AI models for precise patient stratification in cancer immunotherapy.
Keywords:
Artificial intelligenceDigital pathologyImmunotherapyMultiplex bioimaging

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