Hashimoto Thyroiditis, Anti-Parietal Cell Antibodies: Associations With Autoimmune Diseases and Malignancies.

Background: Hashimoto thyroiditis (HT) is an autoimmune disease which may result in extensive damage of the thyroid gland. Chronic atrophic gastritis (CAG), is the most frequent HT-associated disorder, with anti-parietal cell autoantibodies (APCA) being a screening test for autoimmune CAG. The aim of this study was to investigate, in a cohort of HT patients: a) the prevalence of APCA in an attempt to define their clinical phenotype and b) any possible associations of APCA with other autoimmune diseases and malignancies. Methods: This is a single-center, case-control study, conducted at a University Hospital. The study included patients with HT diagnosed between November 2017 and November 2020. Excluded were patients <18 years old, with sonographic features of HT but negative thyroid peroxidase (TPOAbs) or thyroglobulin autoantibodies (TgAbs), Graves' disease, Down or Turner's syndrome. Results: A total of 840 patients with HT were included in the study, from whom 180 (21.4%) had positive APCA. A total of 79 patients (9.4%) had one or more organ-specific autoimmune diseases and 61 (7.3%) had a systemic autoimmune disease. Autoimmune diseases were more frequent in female than in male patients (17.9% versus 10.9%, p = 0.05). APCA-positive patients were older than APCA-negative (54.1 ± 13.5 versus 49.0 ± 14.6, p <0.001) and had more often positive TPOAbs (93.3% versus 83.9%, p=0.001). Gastric neoplasms were documented only in APCA-positive patients (p <0.001). A higher frequency of organ-specific autoimmune diseases was observed in the APCA-positive group (14.4% versus 8%, p = 0.024). In the subgroup of patients with additional autoimmune diseases (n = 140), younger age and positive APCA were independently associated with the presence of organ-specific autoimmunity (OR 0.954, 95% CI 0.927-0.982 and OR 3.100, 95% CI 1.256-7.652, respectively). Papillary thyroid cancer (PTC) occurred in 3.5% of patients (26/29 women). Positive family history for thyroid autoimmunity and negative TPOAbs were the only independent risk factors for PTC among women (OR 3.228, 95% CI 1.173-8.887 and 0.315, 95% 0.113-0.881, respectively). Conclusion: This study reveals for the first time an association of APCA with organ-specific autoimmunity in HT patients. APCA together with patient age were independently associated with the presence of organ-specific autoimmunity. Finally, this study showed an association between APCA and gastric neoplasms in these patients.

Application of Immunocytochemistry on Cell Block Sections for the Investigation of Thyroid Lesions.

OBJECTIVE: To investigate the potential of Classification and Regression Trees (CARTs) for the diagnosis of thyroid lesions based on cell block immunocytochemistry and cytological outcome. STUDY DESIGN: A total of 956 histologically confirmed cases (673 benign and 283 malignant) from patients with thyroid nodules were prepared via liquid-based cytology and evaluated; 4 additional slides were stained for cytokeratin 19 (CK-19), galectin 3 (Gal-3), Hector Battifora mesothelial cell 1 (HBME-1), and thyroglobulin. On the basis of immunocytochemistry and the cytological diagnosis, a CART algorithm was constructed and used for evaluation. RESULTS: The major important factors contributing to the diagnostic CART model were: cytological outcome, CK-19, Gal-3, and HBME-1. The sensitivity and specificity of the cytological diagnosis were 96.27% and 88.26%, respectively (cut-off: category 3 of The Bethesda System [TBS-3]). The introduction of immunocytochemistry and the CART model increased the sensitivity and specificity to 98.88% and 99.11%, respectively. CK-19 presented the best performance for discriminating papillary thyroid carcinomas, followed by HBME-1 and Gal-3. In the TBS-2 cases, CK-19 and, subsequently, Gal-3 were important immunocytochemistry markers. Ultimately, CK-19 and HBME-1 on TBS-5 or TBS-6 cases demonstrated the best results. CONCLUSIONS: The hierarchical structure of the CART model provides a diagnostic algorithm linked with the risk of malignancy at every step of the procedure. It also provides guidance on the use of ancillary examinations as it goes by simple, human understandable rules.