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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
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Valentim, Flavia O. [1] ; Coelho, Barbara P. [1] ; Miot, Helio A. [2] ; Hayashi, Caroline Y. [1] ; Jaune, Danilo T. A. [1] ; Oliveira, Cristiano C. [3] ; Marques, Mariangela E. A. [3] ; Tagliarini, Jose Vicente [4] ; Castilho, Emanuel C. [4] ; Soares, Paula [5, 6, 7] ; Mazeto, Glaucia M. F. S. [1]
Número total de Autores: 11
Afiliação do(s) autor(es):
[1] Sao Paulo State Univ Unesp, Botucatu Med Sch, Internal Med Dept, Botucatu, SP - Brazil
[2] Sao Paulo State Univ Unesp, Botucatu Med Sch, Dept Dermatol, Botucatu, SP - Brazil
[3] Sao Paulo State Univ Unesp, Botucatu Med Sch, Pathol Dept, Botucatu, SP - Brazil
[4] Sao Paulo State Univ Unesp, Botucatu Med Sch, Otolaryngol & Head & Neck Surg Dept, Botucatu, SP - Brazil
[5] Univ Porto, i3S, Porto - Portugal
[6] Univ Porto IPATIMUP, Inst Mol Pathol & Immunol, Canc Signaling & Metab Grp, Porto - Portugal
[7] Univ Porto, Med Fac, Dept Pathol, Porto - Portugal
Número total de Afiliações: 7
Tipo de documento: Artigo Científico
Fonte: ENDOCRINE CONNECTIONS; v. 7, n. 8, p. 907-913, AUG 2018.
Citações Web of Science: 1
Resumo

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. (AU)

Processo FAPESP: 14/10028-2 - Morfometria nuclear e características texturais da cromatina de lesões foliculares tireoidianas
Beneficiário:Bárbara Parente Coelho
Linha de fomento: Bolsas no Brasil - Iniciação Científica