Precision immunoprofiling by image analysis and artificial intelligence

• Published in: Virchows Archiv : an international journal of pathology Vol. 474; no. 4; pp. 511 - 522
• Main Authors: Koelzer, Viktor H., Sirinukunwattana, Korsuk, Rittscher, Jens, Mertz, Kirsten D.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019; Springer Nature B.V
• Subjects: Annual review issues from Virchows Archiv; Artificial Intelligence; Bioinformatics; Biomarkers; Biomarkers, Tumor - analysis; Biomarkers, Tumor - immunology; Cancer; Cancer immunotherapy; Cell interactions; Computation; Computer applications; Data processing; Datasets; Deep learning; Digital imaging; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Immune system; Immunophenotyping - methods; Immunotherapy; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Multiplexing; Oncology; Pathology; Patients; Pattern recognition; PD-L1 protein; Phenotypes; Precision medicine; Precision Medicine - methods; Professional associations; Review; Review Article; Spatial analysis; Surgical instruments; Survival analysis; Digital pathology; Image analysis; Immunotherapy; Machine learning; Personalized medicine; Immuno-oncology; Artificial intelligence
• ISSN: 0945-6317; 1432-2307; 1432-2307
• DOI: 10.1007/s00428-018-2485-z

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.