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Computerized morphometry as an aid in determining the grade of dysplasia and progression to adenocarcinoma in Barrett's esophagus

机译:计算机形态计量学可帮助确定Barrett食管的不典型增生和进展为腺癌

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摘要

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 (PPPPPPP=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.
机译:这项研究的目的是使用计算机形态计量学,以区分发育不良的程度,并预测巴雷特食管(BE)浸润性腺癌的进展。由胃肠道病理学家专家共识论坛对97名BE患者的活检进行形态计量分析。该研究组包括36例不典型增生(ND)阴性的活检,均未进展为癌。 16例不典型增生(IND)和21例低度发育不良(LGD),其中每组3例进展,24例高度发育不良(HGD),其中15例发展为浸润癌。计算机形态计量学用于测量上皮细胞核的大小,形状,质地,对称性和结构分布的指标。通过核假性分层(P = 0.036),多态性(PPPPPPP = 0.027),可以将低度异型增生与ND组最佳区分开。使用神经网络模型在一组新的案例(n = 55)上验证了这些结果,从而区分ND和LGD组的准确度为89%,而区分LGD和HGD组的准确度为86%。在HGD组中,癌症进展间隔的单变量显着预测因子为:核纹理指数(异质性:P = 0.0019,s.d.-OD:P = 0.005)和方向:P = 0.022。通过Cox多变量检验,核纹理(异质性)是进展的唯一独立预测因子(P = 0.004,危险= 11.54)。这项研究表明,计算机形态计量学是确定BE发育异常程度的有效工具。此外,核结构的组织形态计量学定量分析是预测HGD患者进展为浸润性腺癌的强大工具。

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