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MODERN PATHOLOGY
卷 35, 期 2, 页码 186-192出版社
ELSEVIER SCIENCE INC
DOI: 10.1038/s41379-021-00907-6
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The study aimed to improve the accuracy of predicting CSS in FTC patients by combining TERT promoter mutations with the WHO 2017 morphological classification.
Follicular thyroid carcinoma (FTC) has different clinicopathological characteristics than papillary thyroid carcinoma. However, there are no independent systems to predict cancer-specific survival (CSS) in FTC. Telomerase reverse transcriptase (TERT) promoter mutations are associated with tumor aggressiveness. Thus, it could be a potential prognostic marker. The aim of this study was to refine the CSS risk prediction using TERT promoter mutations in combination with the fourth edition of World Health Organization (WHO 2017) morphological classification. We investigated 77 FTC patients between August 1995 and November 2020. Cox regression was used to calculate hazard ratios to derive alternative groups. Disease-free survival (DFS) and CSS predictability were compared using Proportion of variation explained (PVE) and C-index. CSS was significantly different in encapsulated angioinvasive (EA)-FTC patients stratified by TERT promoter mutations [wild-type (WT-TERT) vs. mutant (M-TERT); P < 0.001] but not in minimally invasive (MI)-FTC and widely invasive (WI)-FTC patients (P = 0.691 and 0.176, respectively). We defined alternative groups as follows: Group 1 (MI-FTC with WT-TERT and M-TERT; EA-FTC with WT-TERT), Group 2 (WI-FTC with WT-TERT), and Group 3 (EA-FTC with M-TERT; WI-FTC with M-TERT). Both PVE (22.44 vs. 9.63, respectively) and C-index (0.831 vs. 0.731, respectively) for CSS were higher in the alternative groups than in the WHO 2017 groups. Likewise, both PVE (27.1 vs. 14.9, respectively) and C-index (0.846 vs. 0.794, respectively) for DFS were also higher in the alternative groups than in the WHO 2017 groups. Alternative group harmonizing of the WHO 2017 classification and TERT promoter mutations is effective in predicting CSS in FTC patients, thereby improving DFS predictability.
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