title: Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus.
abstract: The aims of this study were to use computerized morphometry in order to differentiate between the degree of dysplasia and to predict progression to invasive adenocarcinoma in Barrett's esophagus (BE). Biopsies from 97 patients with BE graded by a consensus forum of expert gastrointestinal pathologists were available for morphometrical analysis. The study group included 36 biopsies negative for dysplasia (ND), none of which progressed to carcinoma; 16 indefinite for dysplasia (IND) and 21 low-grade dysplasia (LGD), of which three progressed in each group and 24 high-grade dysplasia (HGD), of which 15 progressed to invasive carcinoma. Computerized morphometry was used for measuring indices of size, shape, texture, symmetry and architectural distribution of the epithelial nuclei. Low-grade dysplasia was best differentiated from the ND group by nuclear pseudostratification (P=0.036), pleomorphism (P<0.01), and chromatin texture (margination, P<0.01) and from the HGD group by nuclear area (P<0.01), pleomorphism (P<0.01), chromatin texture (margination, P<0.01), symmetry (P<0.01), and orientation (P=0.027). These results were validated on a new set of cases (n=55) using a neural network model, resulting in an accuracy of 89% for differentiating between the ND and LGD groups and 86% for differentiating between the LGD and HGD groups. Within the HGD group, univariate significant predictors of the progression interval to carcinoma were: indices of nuclear texture (heterogeneity: P=0.0019, s.d.-OD: P=0.005) and orientation: P=0.022. Nuclear texture (heterogeneity) was the only independent predictor of progression (P=0.004, hazard=11.54) by Cox's multivariate test. This study proposes that computerized morphometry is a valid tool for determining the grade of dysplasia in BE. Moreover, histomorphometric quantification of nuclear texture is a powerful tool for predicting progression to invasive adenocarcinoma in patients with HGD.
authors.affiliation: Department of Pathology, Rhode Island Hospital and Brown University School of Medicine, Providence, RI 02903, USA. esabo@lifespan.org
authors.forename: Edmond
authors.initials: E
authors.lastname: Sabo
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Beck
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Montgomery
authors.forename: Baishali
authors.initials: B
authors.lastname: Bhattacharya
authors.forename: Patricia
authors.initials: P
authors.lastname: Meitner
authors.forename: Ji Yi
authors.initials: JY
authors.lastname: Wang
authors.forename: Murray B
authors.initials: MB
authors.lastname: Resnick
mesh_terms: D000230:Adenocarcinoma / Q000209:etiology*; D001471:Barrett Esophagus / Q000473:pathology*; D004938:Esophageal Neoplasms / Q000209:etiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D011230:Precancerous Conditions / Q000473:pathology*