核形态学在预测弥漫性浸润胶质瘤分级中的应用。

ISRN oncology Pub Date : 2013-08-26 eCollection Date: 2013-01-01 DOI:10.1155/2013/760653
Dibyajyoti Boruah, Prabal Deb
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引用次数: 8

摘要

介绍。可靠地区分肿瘤和非肿瘤标本以及确定弥漫性浸润性胶质瘤(DIGs)的肿瘤等级的能力通常具有挑战性。目的和目标。评估图像形态学在识别DIG区域和预测肿瘤分级方面的效用。材料与方法。采用图像形态学分析30例DIGs和10例对照(CG)的核特征:核长轴(MAJX)、核短轴(MINX)、核面积(NA)、核周长(NP)、核圆度(NR)、核密度(ND)和占核总面积的百分比(%TNA)。结果。除NR外,各组间各项指标差异均有统计学意义,且与肿瘤分级呈正相关(r > 0.7)。HGG的平均值最大,CG的平均值最小。对于NR, CG/HGG差异有统计学意义,而CG/LGG和LGG/HGG差异有统计学意义。脑质瘤的NA分布近似高斯型,范围较小,而胶质瘤的NA分布不规则,范围较大。NA和NP与ND呈显著正相关。结论。图像形态测量学在区分正常组织和肿瘤组织以及区分LGG和HGG病例方面具有巨大的潜力,特别是在微小的立体定向活检中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas.

Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas.

Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas.

Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas.

Introduction. The ability to reliably differentiate neoplastic from nonneoplastic specimen and ascertain the tumour grade of diffusely infiltrating gliomas (DIGs) is often challenging. Aims and Objective. To evaluate utility of image morphometry in identifying DIG areas and to predict tumour grade. Materials and Methods. Image morphometry was used to analyze the following nuclear features of 30 DIGs and 10 controls (CG): major axis of nucleus (MAJX), minor axis of nucleus (MINX), nuclear area (NA), nuclear perimeter (NP), nuclear roundness (NR), nuclear density (ND), and percentage of total nuclear area (%TNA). Results. Statistically significant differences in all parameters, except NR, were observed between all groups, with strong positive correlation with tumour grade (r > 0.7). The mean values were maximum for HGG and minimum for CG. For NR, the difference between CG/HGG was statistically significant, unlike CG/LGG and LGG/HGG. It was observed that NA distributions for CG were nearly Gaussian type with smaller range, while gliomas displayed erratic pattern with larger range. NA and NP exhibited strong positive correlation with ND. Conclusion. Image morphometry has immense potential in being a powerful tool to distinguish normal from neoplastic tissue and also to differentiate LGG from HGG cases, especially in tiny stereotactic biopsies.

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