Follicular thyroid lesions: is there a discriminatory potential in the computerized nuclear analysis?-Reference-Cited by-同舟云学术

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

Published:2018-08 Issue:8 Volume:7 Page:907-913
ISSN:2049-3614
Container-title:Endocrine Connections
language:
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Author:

Valentim Flávia O¹,Coelho Bárbara P¹,Miot Hélio A²,Hayashi Caroline Y¹,Jaune Danilo T A¹,Oliveira Cristiano C³,Marques Mariângela E A³,Tagliarini José Vicente⁴,Castilho Emanuel C⁴,Soares Paula⁵⁶⁷,Mazeto Gláucia M F S¹

Affiliation:

1. 1Internal Medicine Department, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil

2. 2Department of Dermatology, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil

3. 3Pathology Department, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil

4. 4Otolaryngology and Head and Neck Surgery Department, Botucatu Medical School, Sao Paulo State University (Unesp), Botucatu, São Paulo, Brazil

5. 5i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal

6. 6Cancer Signaling and Metabolism Group, Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal

7. 7Department of Pathology, Medical Faculty, University of Porto, Porto, Portugal

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.

Publisher

Bioscientifica

Subject

Endocrinology,Endocrinology, Diabetes and Metabolism,Internal Medicine

Link

https://ec.bioscientifica.com/downloadpdf/journals/ec/7/8/EC-18-0237.xml

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