title: Three-dimensional reconstruction of ductal carcinoma in situ with virtual slides.
abstract: OBJECTIVE
  This study aimed to assess the feasibility of using virtual slides to create 3D histopathological reconstructions to aid in the study of the biology of DCIS.
  
  
  METHODS
  Four μm thick serial sections of formalin fixed paraffin embedded tissue from three cases were cut and mounted onto glass slides, stained with haematoxylin and eosin, then scanned. The three image stacks comprised 30, 115 and 100 scanned sections creating a similar number of virtual slides. The virtual slides were registered using custom 3D software to create 3D tissue volumes. The volumes were annotated to highlight distinct features and 3D visualisations (segmentations) were created to study these features in 3D.
  
  
  RESULTS
  The most time-intensive step was the manual annotation of virtual slides 3D histopathological reconstructions were created of (i) DCIS surrounded by adjacent invasion; (ii) pure DCIS and (iii) a 'normal' lobule.
  
  
  CONCLUSIONS
  3D in silico reconstructions of DCIS were created and more extensive studies can now be done within a realistic timescale. We have identified structural similarities between a benign lobule and DCIS which support the view that much DCIS, apparently in a 'duct' is contained within and expanded lobule. This method has the potential to provide insights into the biology of DCIS.
authors.affiliation: Pathology and Tumour Biology, Leeds Institute of Cancer Studies and Pathology, School of Medicine, University of Leeds, Leeds, UK.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Booth
authors.affiliation: Pathology and Tumour Biology, Leeds Institute of Cancer Studies and Pathology, School of Medicine, University of Leeds, Leeds, UK.|Leeds Teaching Hospitals Trust, Leeds, UK.
authors.forename: Darren
authors.initials: D
authors.lastname: Treanor
authors.affiliation: Pathology and Tumour Biology, Leeds Institute of Cancer Studies and Pathology, School of Medicine, University of Leeds, Leeds, UK.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Roberts
authors.affiliation: School of Computing, University of Leeds, Leeds, UK.
authors.forename: Derek R
authors.initials: DR
authors.lastname: Magee
authors.affiliation: Pathology and Tumour Biology, Leeds Institute of Cancer Studies and Pathology, School of Medicine, University of Leeds, Leeds, UK.
authors.forename: Valerie
authors.initials: V
authors.lastname: Speirs
authors.affiliation: Pathology and Tumour Biology, Leeds Institute of Cancer Studies and Pathology, School of Medicine, University of Leeds, Leeds, UK.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Hanby
mesh_terms: D001943:Breast Neoplasms / Q000473:pathology*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000473:pathology*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D012984:Software
keywords: 3D; DCIS; breast cancer; image analysis