title: Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers.
abstract: BACKGROUND
  Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images.
  
  
  METHODS
  Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system.
  
  
  RESULTS
  Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images.
  
  
  CONCLUSIONS
  Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development.
authors.affiliation: Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA. bilge@drexel.edu
authors.forename: Bilge
authors.initials: B
authors.lastname: Karaçali
authors.forename: Alexandra P
authors.initials: AP
authors.lastname: Vamvakidou
authors.forename: Aydin
authors.initials: A
authors.lastname: Tözeren
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000145:classification / Q000378:metabolism* / Q000473:pathology*; D045744:Cell Line, Tumor; D002462:Cell Membrane / Q000378:metabolism / Q000473:pathology; D002467:Cell Nucleus / Q000378:metabolism / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009363:Neoplasm Proteins / Q000032:analysis*; D010363:Pattern Recognition, Automated / Q000379:methods*