title: Digital pathology: elementary, rapid and reliable automated image analysis.
abstract: OBJECTIVE
  Slide digitalization has brought pathology to a new era, including powerful image analysis possibilities. However, while being a powerful prognostic tool, immunostaining automated analysis on digital images is still not implemented worldwide in routine clinical practice.
  
  
  RESULTS
  Digitalized biopsy sections from two independent cohorts of patients, immunostained for membrane or nuclear markers, were quantified with two automated methods. The first was based on stained cell counting through tissue segmentation, while the second relied upon stained area proportion within tissue sections. Different steps of image preparation, such as automated tissue detection, folds exclusion and scanning magnification, were also assessed and validated. Quantification of either stained cells or the stained area was found to be correlated highly for all tested markers. Both methods were also correlated with visual scoring performed by a pathologist. For an equivalent reliability, quantification of the stained area is, however, faster and easier to fine-tune and is therefore more compatible with time constraints for prognosis.
  
  
  CONCLUSIONS
  This work provides an incentive for the implementation of automated immunostaining analysis with a stained area method in routine laboratory practice.
authors.affiliation: IREC Imaging Platform (2IP), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCL), Brussels, Belgium.
authors.forename: Caroline
authors.initials: C
authors.lastname: Bouzin
authors.affiliation: Service d'anatomopathologie, Cliniques Universitaires Saint-Luc and Cell Biology Unit, de Duve Institute, Université catholique de Louvain, Brussels, Belgium.
authors.forename: Monika L
authors.initials: ML
authors.lastname: Saini
authors.affiliation: Service d'anatomopathologie, Cliniques Universitaires Saint-Luc and Cell Biology Unit, de Duve Institute, Université catholique de Louvain, Brussels, Belgium.
authors.forename: Kyi-Kyi
authors.initials: KK
authors.lastname: Khaing
authors.affiliation: Center for Applied Molecular Technologies (CTMA), Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain (UCL), Brussels, Belgium.
authors.forename: Jérôme
authors.initials: J
authors.lastname: Ambroise
authors.affiliation: Service d'anatomopathologie, Cliniques Universitaires Saint-Luc and Cell Biology Unit, de Duve Institute, Université catholique de Louvain, Brussels, Belgium.
authors.forename: Etienne
authors.initials: E
authors.lastname: Marbaix
authors.affiliation: Center for Molecular Imaging, Radiotherapy and Oncology, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCL), Brussels, Belgium.
authors.forename: Vincent
authors.initials: V
authors.lastname: Grégoire
authors.affiliation: Center for Molecular Imaging, Radiotherapy and Oncology, Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCL), Brussels, Belgium.
authors.forename: Vanesa
authors.initials: V
authors.lastname: Bol
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D002277:Carcinoma / Q000175:diagnosis*; D002291:Carcinoma, Papillary; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis*; D006258:Head and Neck Neoplasms / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D015203:Reproducibility of Results; D000077195:Squamous Cell Carcinoma of Head and Neck; D000077273:Thyroid Cancer, Papillary; D013964:Thyroid Neoplasms / Q000175:diagnosis*
chemical_list: D014408:Biomarkers, Tumor
keywords: automation; biological markers; digital pathology; image analysis; immunohistochemistry; prognosis; reproducibility