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自动图像分析中黑素细胞性皮肤肿瘤的诊断组织成分

Diagnostic tissue elements in melanocytic skin tumors in automated image analysis.

作者信息

Gerger Armin, Smolle Josef

机构信息

Department of Dermatology, University of Graz, Austria.

出版信息

Am J Dermatopathol. 2003 Apr;25(2):100-6. doi: 10.1097/00000372-200304000-00002.

DOI:10.1097/00000372-200304000-00002
PMID:12652190
Abstract

In tissue counter analysis, digital images are divided into subregions (elements), and the digital information in each element is used for statistical analysis. In this study, we assessed the morphologic details of tissue elements that have turned out to be of diagnostic significance in the discrimination of benign common nevi and malignant melanoma. After creation of a data set based on a total of 12,000 cellular elements obtained from 100 benign common nevi and 100 malignant melanomas, classification and regression tree (CART) analysis was performed to differentiate between cellular elements of nevi and melanoma. In a second step, the slides were re-evaluated by the decision tree; cellular elements suggestive either for benign common nevi or for malignant melanoma were highlighted on zoomed images of the whole sections, and the individual elements were displayed in galleries. Eight groups of elements (so-called terminal nodes) seemed to indicate benign common nevi, whereas seven terminal nodes were suggestive for malignant melanoma. The elements of nodes suggestive for benign nevi largely contained nevus cells with amphiphilic cytoplasm intermingled with fibrillary material, whereas the elements of the nodes suggestive for malignant lesions often showed hyperchromatism, perinuclear halos, heavy pigmentation, or a lymphohistiocytic infiltrate. Tissue counter analysis automatically detects tissue elements that are in accordance with morphologic criteria used in conventional histopathology for diagnostic discrimination.

摘要

在组织计数分析中,数字图像被划分为子区域(单元),每个单元中的数字信息用于统计分析。在本研究中,我们评估了组织单元的形态学细节,这些细节在鉴别良性普通痣和恶性黑色素瘤中已被证明具有诊断意义。在基于从100个良性普通痣和100个恶性黑色素瘤中获取的总共12000个细胞单元创建数据集后,进行分类回归树(CART)分析以区分痣和黑色素瘤的细胞单元。第二步,通过决策树对切片进行重新评估;在整个切片的放大图像上突出显示提示良性普通痣或恶性黑色素瘤的细胞单元,并在图库中展示各个单元。八组单元(所谓的终末节点)似乎提示良性普通痣,而七个终末节点提示恶性黑色素瘤。提示良性痣的节点单元主要包含具有两亲性细胞质并与纤维状物质混合的痣细胞,而提示恶性病变的节点单元通常表现为核染色质增多、核周晕、色素沉着加重或淋巴细胞组织细胞浸润。组织计数分析自动检测符合传统组织病理学用于诊断鉴别的形态学标准的组织单元。

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