A study of cytological and histopathological correlation in nodular goiter and its associated lesions with emphasis on morphological patterns and Epidemiology

Abstract

Background: To evaluate the different patterns of thyroid lesions associated with MNG in surgically resected specimens and biopsies received at department of pathology and correlate the various histological and cytological diagnosis to ensure the usefulness of cytology prior histopathology. Methods: The study period is from 2017 to 2020 January carried out in Department of Pathology, GVPIHC&MT on the thyroidectomy specimens for detailed analysis. Prior cytology and histomorphological diagnosis was recorded and analysed. Results: Various morphological patterns were noted; most common were calcification (85%), cystic change (80%) and Sanderson polsters (75%). 19 nonneoplastic lesions associated with MNG were noted in this study. Most common lesion noted was Hashimoto’s thyroiditis constituting 19 cases seen in the age group of 41-60 yrs. 15 benign lesions associated with nodular goiter were noted in this study. 22 malignant lesions were observed to be associated with nodular goiter with most common malignant lesion being papillary thyroid carcinoma followed by its variant papillary micro-carcinoma. The malignant lesions had lymphnodal enlargement with metastases in 9 cases constituting 7 cases of N1a and 2 cases of N1b.TBSRTC Category – I included 10 cases with inadequate smears for reporting. Most of them i.e. 81 cases constituting 63.28% were benign and were included under Category II. Only 7 cases were indeterminate lesions. Category IV included the next highest reported cases after Category II including 17 cases constituting 13.28%. Only 3 cases were included under suspicious category/category V. 7.8% i.e. 10 cases were reported to be malignant. Conclusions: Taking into consideration histopathology report as a gold standard, correlation of cytological finding with histopathology finding showed 90% sensitivity, 98.8% specificity with 90% positive predictive value.