Nuclear morphometry and texture analysis on cytological smears of thyroid neoplasms: a study of 50 cases.

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Nuclear morphometry and texture analysis on cytological smears of thyroid neoplasms: a study of 50 cases.

Malays J Pathol. 2017 Apr;39(1):33-37

Authors: Deka L, Gupta S, Gupta R, Gupta K, Kaur CJ, Singh S S

Abstract
BACKGROUND: Fine needle aspiration cytology (FNAC) is a reliable and reproducible diagnostic technique for thyroid lesions with certain limitations. Computed morphometric methods have been introduced with a view to improve the diagnostic yield of thyroid aspirates. However, a review of the existing literature revealed conflicting reports regarding morphometric parameters in thyroid neoplasms.
MATERIALS AND METHODS: This study included 50 cases of thyroid lesions (20 cases of colloid goitre, 15 of follicular adenoma, 5 of follicular carcinoma and 10 papillary carcinomas). Digital images of cytologic smears of these cases were captured using a dedicated photomicrography system and nuclear profiles traced manually. With self-designed image analysis software, nuclear morphometric measurements, including texture analysis, were performed. Discriminant analysis was performed including the morphometric parameters and percentage of correctly classified nuclei noted.
RESULTS: Nuclear morphometry parameters showed that papillary thyroid carcinoma had the highest perimeter, area, radius and elongation factor compared to other thyroid lesions. Discriminant analysis revealed that altogether 77.9% of cells could be correctly classified to their lesion category based on the nuclear morphometric and textural parameters. Of the neoplastic cases, 84.5% of cells of follicular neoplasms and 72.5% of papillary carcinoma were classified to the respective category.
CONCLUSION: Nuclear morphometry, including texture analysis, can assist in the cytologic diagnosis of thyroid lesions, considering the high degree of accuracy of classification. Further studies and methodological refinements can achieve higher accuracy.

PMID: 28413203 [PubMed - in process]

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