Follicular Lesions with Papillary Nuclear Characteristics: Differences in Chromatin Detected by Computerized Image Analysis

Abstract

Objective: Follicular lesions of the thyroid with papillary carcinoma nuclear characteristics are classified as infiltrative follicular variant of papillary thyroid carcinoma-FVPTC (IFVPTC), encapsulated/ well demarcated FVPTC with tumour capsular invasion (IEFVPTC), and the newly described category "non-invasive follicular thyroid neoplasm with papillary-like nuclear features" (NIFTP) formerly known as non-invasive encapsulated FVPTC. This study evaluated whether computerized image analysis can detect nuclear differences between these three tumour subtypes. Materials and methods: Slides with histological material from 15 cases of NIFTP and 33 cases of FVPTC subtypes (22 IEFVPTC, and 11 IFVPTC) were analyzed using the Image J image processing program. Tumour cells were compared for both nuclear morphometry and chromatin textural characteristics. Results: Nuclei from NIFTP and IFVPTC tumours differed in terms of chromatin textural features (grey intensity): mean (92.37 ± 21.01 vs 72.99 ± 14.73, p = 0.02), median (84.93 ± 21.17 vs 65.18 ± 17.08, p = 0.02), standard deviation (47.77 ± 9.55 vs 39.39 ± 7.18; p = 0.02), and coefficient of variation of standard deviation (19.96 ± 4.01 vs 24.75 ± 3.31; p = 0.003). No differences were found in relation to IEFVPTC. Conclusion: Computerized image analysis revealed differences in nuclear texture between NIFTP and IFVPTC, but not for IEFVPTC. Arch

Digital analyses of nuclear features can help discriminate among non‐invasive follicular thyroid neoplasm with papillary‐like nuclear features, papillary thyroid carcinoma follicular subtype, and follicular carcinoma in cytological specimens

“…This is consistent with the concept of nuclear size as an important feature for discriminating benign from malignant cells. 13 We demonstrated the importance of evaluating nuclear parameters using digital pathology, consistent with the findings of studies analysing follicular cells in cytological [9][10][11]14 and histological [15][16][17] samples. Studies with histological samples are more common in the literature.…”

“…Studies with histological samples are more common in the literature. [15][16][17] In cytological samples, digital pathology has been shown to increase the diagnostic accuracy of malignancy in indeterminate nodules 10 and help differentiate malignant from benign nodules because, in the former, the minimum nuclear diameter, maximum nuclear diameter, perimeter, and nuclear area were more significant. 9 These findings, combined with the potential applications artificial intelligence in digital pathology, offer a promising outlook for improving diagnostic accuracy in thyroid cytology.…”

“…9 These findings, combined with the potential applications artificial intelligence in digital pathology, offer a promising outlook for improving diagnostic accuracy in thyroid cytology. 18 In studies that evaluated histological samples, Coelho et al 15 used ImageJ software and found a difference in the texture of nuclear chromatin when comparing NIFTP and infiltrative follicular subtype papillary thyroid carcinoma; the former presented lighter nuclei with more heterogeneous staining, but this difference was and normal thyroid cells using morphometric parameters (nucleus size and chromatin distribution). Normal thyroid cells had smaller nuclei and less variation between nuclei (entropy), while FC had the most significant irregularity in the distribution of sizes and entropy.…”

Indeterminate thyroid cytology: detecting malignancy using analysis of nuclear images

“…Studies with larger samples would probably include NIFTP cases ( 23 ), which were not found in our group of patients and could have nuclear alterations but turn out to be 'non-malignant' in the final histological examination. In this sense, we compared recently the histological material of NIFTP, infiltrative FVPC, and encapsulated/well-demarcated FVPC with tumor capsular invasion, using CANI and observed nuclear textural differences between NIFTP and infiltrative FVPC but not between the former and encapsulated/well-demarcated FVPC ( 24 ). In addition to significantly larger samples, it would also be interesting to apply this model to other Bethesda categories, such as benign.…”

Trends in AI-powered classification of thyroid neoplasms based on histopathology images, a systematic review

“…Other than DL, some studies adopted a traditional image analysis approach for thyroid nodule classification based on nuclear morphology characterization (4, 5). Valentim et al used computerized nuclear morphometry and texture analysis of histology images coupled with CRT (Classification and Regression Trees) to distinguish follicular pattern thyroid lesions.…”