ArticleEndocrine connections2018
Follicular thyroid lesions: is there a discriminatory potential in the computerized nuclear analysis?
Article in Endocrine connections, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparative Assessment of Agreement in Uniformity Analyses across Quality Control Software Platforms.World journal of nuclear medicine · 2025Article
- Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model.Journal of pathology and translational medicine · 2024Article
- Trends in AI-powered Classification of Thyroid Neoplasms Based on Histopathology Images - a Systematic Review.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023Article
- Indeterminate thyroid cytology: detecting malignancy using analysis of nuclear images.Endocrine connections · 2021Article
- Follicular Lesions with Papillary Nuclear Characteristics: Differences in Chromatin Detected by Computerized Image Analysis.Archives of endocrinology and metabolism · 2021Article
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Authors and funding
11 authors.
Funding
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Abstract
backgroundComputerized 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.
methodsWe 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.
resultsWe 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.
conclusionThe 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.
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