OBJECTIVES: The microscopic distinction between benign and malignant thyroid lesions in clinical practice is still largely based on conventional histology. This study was performed to evaluate the diagnostic value of galectin-3 (Gal-3), Hector Battifora mesothelial-1 (HBME-1), cytokeratin (CK)-19, CBP P300-interacting transactivator with glutamic acid E- and aspartic acid D-rich C-terminal domain (CITED-1), fibronectin (FN)-1, peroxisome proliferator-activated receptor (PPAR)-gamma, and intracellular sodium/iodide symporter (iNIS) immunostaining in a large panel of thyroid neoplasms. Our study differed from earlier ones with regard to the identification of optimal semiquantitative cut-off levels using receiver operator curve (ROC) analysis and hierarchical cluster analysis. METHODS: We used tissue arrays containing 177 thyroid tissues: 100 benign tissues (including normal thyroid, Graves disease, multinodular goiter, and follicular adenoma (FA)) and 77 thyroid carcinomas (including papillary thyroid carcinoma (PTC), follicular thyroid carcinoma, and follicular variant of PTC (FVPTC)). Antibody staining was scored semiquantitatively based on the ROC analyses and with hierarchical cluster analysis. RESULTS: In general, we found overexpression of FN-1, CITED-1, Gal-3, CK-19, HBME-1, and iNIS in malignant thyroid lesions. Gal-3, FN-1, and iNIS had the highest accuracy in the differential diagnosis of follicular lesions. A panel of Gal-3, FN-1, and iNIS, identified by hierarchical cluster analysis, had a 98% accuracy to differentiate between FA and malignant thyroid lesions. In addition, HBME-1 was found to be useful in the differentiation between FA and FVPTC (accuracy 88%). CONCLUSION: We conclude that identifying optimal antibody panels with cluster analysis increases the diagnostic value in the differential diagnosis of thyroid neoplasms, the combination of FN-1, Gal-3, and iNIS having the best accuracy (98%).
OBJECTIVES: The microscopic distinction between benign and malignant thyroid lesions in clinical practice is still largely based on conventional histology. This study was performed to evaluate the diagnostic value of galectin-3 (Gal-3), Hector Battifora mesothelial-1 (HBME-1), cytokeratin (CK)-19, CBP P300-interacting transactivator with glutamic acid E- and aspartic acid D-rich C-terminal domain (CITED-1), fibronectin (FN)-1, peroxisome proliferator-activated receptor (PPAR)-gamma, and intracellular sodium/iodide symporter (iNIS) immunostaining in a large panel of thyroid neoplasms. Our study differed from earlier ones with regard to the identification of optimal semiquantitative cut-off levels using receiver operator curve (ROC) analysis and hierarchical cluster analysis. METHODS: We used tissue arrays containing 177 thyroid tissues: 100 benign tissues (including normal thyroid, Graves disease, multinodular goiter, and follicular adenoma (FA)) and 77 thyroid carcinomas (including papillary thyroid carcinoma (PTC), follicular thyroid carcinoma, and follicular variant of PTC (FVPTC)). Antibody staining was scored semiquantitatively based on the ROC analyses and with hierarchical cluster analysis. RESULTS: In general, we found overexpression of FN-1, CITED-1, Gal-3, CK-19, HBME-1, and iNIS in malignant thyroid lesions. Gal-3, FN-1, and iNIS had the highest accuracy in the differential diagnosis of follicular lesions. A panel of Gal-3, FN-1, and iNIS, identified by hierarchical cluster analysis, had a 98% accuracy to differentiate between FA and malignant thyroid lesions. In addition, HBME-1 was found to be useful in the differentiation between FA and FVPTC (accuracy 88%). CONCLUSION: We conclude that identifying optimal antibody panels with cluster analysis increases the diagnostic value in the differential diagnosis of thyroid neoplasms, the combination of FN-1, Gal-3, and iNIS having the best accuracy (98%).
Authors: Connie G Chiu; Scott S Strugnell; Obi L Griffith; Steven J M Jones; Allen M Gown; Blair Walker; Ivan R Nabi; Sam M Wiseman Journal: Am J Pathol Date: 2010-04-02 Impact factor: 4.307
Authors: Leandro Luongo de Matos; Adriana Braz Del Giglio; Carolina Ogawa Matsubayashi; Michelle de Lima Farah; Auro Del Giglio; Maria Aparecida da Silva Pinhal Journal: Diagn Pathol Date: 2012-08-13 Impact factor: 2.644