N Grabe1,2, W Roth3, S Foersch3. 1. Nationales Centrum für Tumorerkrankungen und Medizinische Onkologie, Universitätsklinikum Heidelberg, Heidelberg, Deutschland. niels.grabe@gmail.com. 2. Tissue Imaging & Analysis Center, Universität Heidelberg, Im Neuenheimer Feld 267, 69120, Heidelberg, Deutschland. niels.grabe@gmail.com. 3. Institut für Pathologie, Universitätsmedizin Mainz, Mainz, Deutschland.
Abstract
BACKGROUND: Immuno-oncology requires objective and standardized methods for measuring immune cell infiltrates for therapy selection and clinical trials. METHODS: Current approaches in applying digital pathology in immuno-oncology and developments in computational image analysis were analyzed. RESULTS: Since 2008, digital pathology has had an ever increasing importance in immuno-oncology. It is currently the only technology allowing the systematic and cost-effective quantitative spatial immune-profiling of patients. The analysis of immunological biomarkers requires integrated staining and image analysis strategies from single- to multistain on slide stacks. Statistical limits of the hypothesis to be tested have to be taken into account. Digital image analysis opens a new technological role for pathology in immuno-oncology and thereby serves as a key technological driver. CONCLUSION: Digital pathology delivers objective and quantitative data on the tumor microenvironment. But currently, a fully automatic, high-throughput analytics capability is still missing. Deep learning is the remedy for this, as it improves image analysis with increasing data availability. This requires the creation of systematic data collections but will in the end deliver standardized and automatic immunological analyses.
BACKGROUND: Immuno-oncology requires objective and standardized methods for measuring immune cell infiltrates for therapy selection and clinical trials. METHODS: Current approaches in applying digital pathology in immuno-oncology and developments in computational image analysis were analyzed. RESULTS: Since 2008, digital pathology has had an ever increasing importance in immuno-oncology. It is currently the only technology allowing the systematic and cost-effective quantitative spatial immune-profiling of patients. The analysis of immunological biomarkers requires integrated staining and image analysis strategies from single- to multistain on slide stacks. Statistical limits of the hypothesis to be tested have to be taken into account. Digital image analysis opens a new technological role for pathology in immuno-oncology and thereby serves as a key technological driver. CONCLUSION: Digital pathology delivers objective and quantitative data on the tumor microenvironment. But currently, a fully automatic, high-throughput analytics capability is still missing. Deep learning is the remedy for this, as it improves image analysis with increasing data availability. This requires the creation of systematic data collections but will in the end deliver standardized and automatic immunological analyses.
Authors: Kai Klintrup; Johanna M Mäkinen; Saila Kauppila; Päivi O Väre; Jukka Melkko; Hannu Tuominen; Karoliina Tuppurainen; Jyrki Mäkelä; Tuomo J Karttunen; Markus J Mäkinen Journal: Eur J Cancer Date: 2005-10-18 Impact factor: 9.162
Authors: S Michel; A Benner; M Tariverdian; N Wentzensen; P Hoefler; T Pommerencke; N Grabe; M von Knebel Doeberitz; M Kloor Journal: Br J Cancer Date: 2008-11-04 Impact factor: 7.640
Authors: Daniel Kazdal; Eugen Rempel; Cristiano Oliveira; Michael Allgäuer; Alexander Harms; Kerstin Singer; Elke Kohlwes; Steffen Ormanns; Ludger Fink; Jörg Kriegsmann; Michael Leichsenring; Katharina Kriegsmann; Fabian Stögbauer; Luca Tavernar; Jonas Leichsenring; Anna-Lena Volckmar; Rémi Longuespée; Hauke Winter; Martin Eichhorn; Claus Peter Heußel; Felix Herth; Petros Christopoulos; Martin Reck; Thomas Muley; Wilko Weichert; Jan Budczies; Michael Thomas; Solange Peters; Arne Warth; Peter Schirmacher; Albrecht Stenzinger; Mark Kriegsmann Journal: Transl Lung Cancer Res Date: 2021-04