医学
细胞病理学
贝塞斯达系统
甲状腺肿瘤
甲状腺癌
甲状腺
细针穿刺
甲状腺切除术
恶性肿瘤
放射科
普通外科
细胞学
内科学
病理
活检
作者
Ke Jiang,Lei Jianyong,Ying Liu,Genpeng Li,Song Linlin,Zhihui Li,Jinnan Li,Xueying Su,Yong Jiang,Zhu Jingqiang
摘要
AIMS: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) has been widely adopted since its introduction. In this study, we aimed to report our experience using this reporting system at a College of American Pathologists (CAP)-accredited hospital laboratory in a large series of Chinese patients. METHODS: All patients who underwent preoperative ultrasound-guided thyroid fine-needle aspiration (FNA) from January 2011 to August 2016 were retrospectively analyzed. Thyroid FNAs were classified according to the Bethesda System. For patients who underwent subsequent surgery at our institution, the diagnostic performance of the preoperative FNA was further analyzed according to four different calculation criteria. All of the follicular variants of papillary thyroid carcinoma specimens were reviewed to exclude NIFTP (noninvasive follicular thyroid neoplasm with papillary-like nuclear features). RESULTS: A total of 13 351 thyroid FNAs were included in this analysis. Of the 12 530 sampled patients, 3594 (28.7%) underwent thyroidectomy, and the malignancy rates for each cytological category were as follows: 66.7% unsatisfactory, 14.2% benign, 53.5% undetermined significance, 30.2% follicular neoplasm, 82.0% suspicious, and 99.1% malignant. Only 12 (0.36% of all PTC) patients were reclassified as having NIFTP. The sensitivities of the preoperative FNAs were all above 95.0% and were as high as 99.0%. The specificities ranged from 50.3% to 63.9%, depending on which criteria were used. The positive predictive value was 95.4% for criteria 1 and 2 and was 94.2% for criteria 3 and 4. The negative predictive values ranged from 64.5% to 85.8%. The diagnostic accuracies all exceeded 90.0%, with the highest being 94.8%. CONCLUSION: This study revealed the great efficacy and accuracy of TBSRTC in a large Chinese population for the first time.
科研通智能强力驱动
Strongly Powered by AbleSci AI