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Precision immunoprofiling by image analysis and artificial intelligence.

Viktor H Koelzer1,2, Korsuk Sirinukunwattana3, Jens Rittscher3,4,5

  • 1Institute of Cancer and Genomic Science, University of Birmingham, 6 Mindelsohn Way, Birmingham, B15 2SY, UK. vkoelzer@well.ox.ac.uk.

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Summary
This summary is machine-generated.

Computational pathology and artificial intelligence are crucial for developing new assays to predict patient response to immunotherapy. These tools enhance immune-oncology biomarker assessment and patient stratification for precision medicine.

Keywords:
Artificial intelligenceDigital pathologyImage analysisImmuno-oncologyImmunotherapyMachine learningPersonalized medicine

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Area of Science:

  • Computational pathology
  • Artificial intelligence
  • Bioinformatics
  • Immuno-oncology

Background:

  • Clinical success of immunotherapy necessitates advanced prognostic and predictive assays for patient selection.
  • Current methods require enhancement for accurate patient stratification in cancer treatment.

Purpose of the Study:

  • To critically assess computational pathology and AI approaches for standardized immune-oncology biomarker assessment.
  • To explore the role of spatial analysis and multiplexing technologies in predicting treatment response.
  • To discuss the integration of bioinformatics and multiomics data for precision medicine.

Main Methods:

  • Review of computational pathology and AI methodologies in immuno-oncology.
  • Analysis of spatial immunoprofiling and multiplexing technologies.
  • Examination of machine learning (ML) and deep learning applications.
  • Integration of morphological phenotypes with multiomics datasets.

Main Results:

  • Computational pathology and AI offer standardized methods for assessing biomarkers like PD-L1 and immune cell infiltrates.
  • Spatial analysis of tumor-immune interactions predicts patient response to cancer therapy.
  • Integrated bioinformatics enables combining morphological data with multiomics for precision medicine.

Conclusions:

  • Synergies between surgical pathology and computational analysis will improve patient stratification in immuno-oncology.
  • Future clinical needs require dedicated research at the pathology-bioinformatics interface.
  • Integrating data sciences and digital image analysis into pathologist education is essential.