Nuclear grading of breast carcinoma by image analysis. Classification by multivariate and neural network analysis.
Nuclear grading of breast carcinoma by image analysis. Classification by multivariate and neural network analysis.
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发表时间：
1991-04
影响因子：
3.5
通讯作者：
A. Dawson;Austin Re;Weinberg Ds
中科院分区：
文献类型：
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作者：
A. Dawson;Austin Re;Weinberg Ds
核分级作为乳腺癌的预后指标受到观察者间差异的限制。图像分析和自动化细胞分类的进展提供了解决这个问题的一种方法。作者使用CAS-100（细胞分析系统。埃尔姆赫斯特，IL）系统测量和分析来自35个乳腺癌（高、中、低分化）以及良性病变的细胞学制备物的核形态和纹理特征。形态学和马尔可夫纹理特征数据从乳腺癌细胞核的各个等级组成的训练集，然后使用多变量（贝叶斯）分析建立分类标准，并训练神经网络系统。两个系统都测试了对单个核的核等级进行分类的能力。计算机分类和人类观察者使用贝叶斯或神经网络分析对单个细胞核分配的等级之间有很好的一致性。三十一个未知的情况下，这是由一个观察员分配一个整体的等级，然后通过计算机进行分析，和一个整体的等级分配的基础上最常见的核的等级。使用这种方法，两种分类系统都能够将“正确”等级分配给低级别病变（约70%正确），而不是高级别肿瘤（约20%）。计算机分配高级别肿瘤的困难是由这些肿瘤的核异质性解释的（即，尽管与低级别肿瘤相比，高级别细胞核的百分比增加，但高级别细胞核通常不占优势）。作者提出这项研究，以证明使用图像分析作为核分级的客观手段的可行性。将需要进一步的研究，以建立基于计算机分析成像数据的总体核分级的标准。
The use of nuclear grade as a prognostic indicator for breast carcinoma has been limited by interobserver variability. Advances in image analysis and automated cell classification offer one approach to this problem. The authors used the CAS-100 (Cell Analysis System. Elmhurst, IL) system to measure and analyze nuclear morphometric and texture features of cytologic preparations from 35 breast carcinomas (well, moderate, and poorly differentiated) as well as benign lesions. Morphometric and Markovian texture feature data from breast cancer nuclei of various grades comprised a training set, which was then used to establish classification criteria by multivariate (Bayesian) analysis and to train a neural network system. Both systems were tested for the ability to classify the nuclear grade of individual nuclei. There was good agreement between computer classification and the grade assigned by human observer to individual nuclei using either Bayesian or neural network analysis. Thirty-one unknown cases, which were assigned an overall grade by an observer, were then analyzed by computer, and an overall grade assigned based on the grade of nucleus most frequently present. Using this method, both classification systems were able to assign a "correct" grade to low-grade lesions (approximately 70% correct) more often than to high-grade tumors (approximately 20%). Difficulty in computer assignment of high-grade tumors was explained by nuclear heterogeneity in these tumors (i.e., although the percentage of high-grade nuclei was increased compared with that of low-grade tumors, high-grade nuclei frequently did not predominate). The authors present this study to demonstrate the feasibility of using image analysis as an objective means of nuclear grading. Further studies will be needed to establish criteria for assigning overall nuclear grade based on computer analysis of imaging data.