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滤泡性甲状腺病变：计算机化核分析是否具有鉴别潜力？

Follicular thyroid lesions: is there a discriminatory potential in the computerized nuclear analysis?

作者信息

Valentim Flávia O, Coelho Bárbara P, Miot Hélio A, Hayashi Caroline Y, Jaune Danilo T A, Oliveira Cristiano C, Marques Mariângela E A, Tagliarini José Vicente, Castilho Emanuel C, Soares Paula, Mazeto Gláucia M F S

机构信息

Internal Medicine Department, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil.

Department of Dermatology, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil.

出版信息

Endocr Connect. 2018 Aug;7(8):907-913. doi: 10.1530/EC-18-0237. Epub 2018 Jul 4.

DOI:10.1530/EC-18-0237
PMID:29973373
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6063880/
Abstract

BACKGROUND

Computerized image analysis seems to represent a promising diagnostic possibility for thyroid tumors. Our aim was to evaluate the discriminatory diagnostic efficiency of computerized image analysis of cell nuclei from histological materials of follicular tumors.

METHODS

We studied paraffin-embedded materials from 42 follicular adenomas (FA), 47 follicular variants of papillary carcinomas (FVPC) and 20 follicular carcinomas (FC) by the software ImageJ. Based on the nuclear morphometry and chromatin texture, the samples were classified as FA, FC or FVPC using the Classification and Regression Trees method.

RESULTS

We observed high diagnostic sensitivity and specificity rates (FVPC: 89.4% and 100%; FC: 95.0% and 92.1%; FA: 90.5 and 95.5%, respectively). When the tumors were compared by pairs (FC vs FA, FVPC vs FA), 100% of the cases were classified correctly.

CONCLUSION

The computerized image analysis of nuclear features showed to be a useful diagnostic support tool for the histological differentiation between follicular adenomas, follicular variants of papillary carcinomas and follicular carcinomas.

摘要

背景

计算机图像分析似乎是甲状腺肿瘤诊断的一种有前景的可能性。我们的目的是评估对滤泡性肿瘤组织学材料中的细胞核进行计算机图像分析的鉴别诊断效率。

方法

我们使用ImageJ软件研究了42例滤泡性腺瘤（FA）、47例乳头状癌滤泡变体（FVPC）和20例滤泡癌（FC）的石蜡包埋材料。基于核形态测量和染色质纹理，使用分类与回归树方法将样本分类为FA、FC或FVPC。

结果

我们观察到较高的诊断敏感性和特异性率（FVPC分别为89.4%和100%；FC分别为95.0%和92.1%；FA分别为90.5%和95.5%）。当对肿瘤进行两两比较时（FC与FA、FVPC与FA），100%的病例分类正确。

结论

细胞核特征的计算机图像分析显示是滤泡性腺瘤、乳头状癌滤泡变体和滤泡癌组织学鉴别诊断的有用支持工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/b3b84a748ae0/ec-7-907-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/42d2cdb9619e/ec-7-907-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/b823870be4d7/ec-7-907-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/b3b84a748ae0/ec-7-907-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/42d2cdb9619e/ec-7-907-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/b823870be4d7/ec-7-907-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2e9/6063880/b3b84a748ae0/ec-7-907-g003.jpg

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