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

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.

Indeterminate thyroid cytology: detecting malignancy using analysis of nuclear images

“…Limitations of this study could be possible inter-observer variability in results using this method, the time required for nuclear image analysis, the number of nodes required for the design and size of the classification trees, as well the lack of NIFTP cases, and the sample size. In regard to inter-observer variability, we observed high reproducibility levels using CANI in this study as there was almost complete agreement between both evaluators in 10 of the 12 parameters analyzed ( 7 ). However, the model was not validated for the entire cohort of patients in order to test that reproducibility.…”

“…However, in lesions classified by Bethesda as indeterminate and composing mainly of AUS/FLUS and FN/SFN, results have not been fully conclusive ( 12 , 20 , 21 ). Recently, Valentim et al , 2018, using CANI to evaluate thyroid tumor tissues which frequently present these cytological diagnoses, observed high percentages of sensitivity and final diagnostic specificity ( 7 ). Their findings prompted us to undertake the present study in which the same tool was used to evaluate smears from thyroid nodules with indeterminate cytological diagnoses in which high percentages of correct classification regarding the real nature of the lesion were also observed.…”

“…The number of nuclei to be evaluated in each photo was determined through a pilot statistical study that estimated the minimum quantity from which the addition of more nuclei would not alter the subsequent statistical analysis. This study was based on the standard error stabilization of 5% for each variable, concluding that 60 nuclei per photo would be sufficient and thus warranting the precision of the measures, independently of the sampled area ( 7 ). As the nuclear contour of photographs containing cytological material is performed manually and could lead to inter-observer differences, method reproducibility was tested by analyzing 20 slides by both evaluators.…”

“…Secondary indicators were also considered, such as coefficient of variation (CV) of area, CV-mean intensity, CV-STDEV, CV-perimeter, CV-circularity, CV-feret, CV-median intensity, CV-AR, CV-round, CV-solidity, CV-fractal, CV-entropy and CV-RA. The program expresses these parameters in pixels, which can then be converted into microns (µm) ( 7 ).…”

“…In this sense, the evaluation of nuclear morphometric and textural parameters by computerized image analysis has been successfully used in tumor tissues from different neoplasias, including thyroid ( 7 ), proving to be an accessible, reproducible and low-cost tool. Although other studies have used this technique on thyroid cytological material ( 8 , 9 , 10 , 11 , 12 , 13 ), in general, they have not focused on the indeterminate category.…”