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首页> 外文期刊>Ultrasound quarterly. >Risk Threshold Algorithm for Thyroid Nodule Management Demonstrates Increased Specificity and Diagnostic Accuracy as Compared With American College of Radiology Thyroid Imaging, Reporting and Data System; Society of Radiologists in Ultrasound; and American Thyroid Association Management Guidelines
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Risk Threshold Algorithm for Thyroid Nodule Management Demonstrates Increased Specificity and Diagnostic Accuracy as Compared With American College of Radiology Thyroid Imaging, Reporting and Data System; Society of Radiologists in Ultrasound; and American Thyroid Association Management Guidelines

机译：甲状腺结节管理的风险阈值算法表明与美国放射学院甲状腺成像，报告和数据系统相比，较高的特异性和诊断准确性; 超声波学家社会; 和美国甲状腺协会管理指南

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We studied diagnostic performance of an algorithm guiding thyroid nodule management using a malignancy risk model as compared with extant management guidelines. Single-institution, retrospective study was performed with sequential cases from pathology registry from 2012 to 2015. Seventy-eight patients were enrolled, with benign and malignant groups defined by aspiration results. Risk Threshold Algorithm determined management based off of a logistic regression model and a risk threshold. American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS), Society of Radiologists in Ultrasound (SRU), and American Thyroid Association (ATA) guidelines were used in comparison. Sensitivity, specificity, positive/negative predictive values, receiver operating characteristic (ROC) values were derived, with significance assessed via McNemar and permutation tests. Forty-four benign nodules and 40 papillary thyroid carcinomas were included. Risk Threshold Algorithm area under the ROC curve was 0.80 versus 0.59 (ACR TI-RADS), 0.49 (SRU), and 0.44 (ATA); all areas under the ROC curve differences were statistically significant. Risk Threshold Algorithm demonstrates sensitivity, specificity, positive predictive value, and negative predictive values of 63%, 91%, 86%, and 73% at the risk threshold maximizing diagnostic performance, compared with 85%, 39%, 56%, and 74% (ACR TI-RADS); 85%, 18%, 50%, and 57% (SRU); and 89%, 11%, 50%, and 83% (ATA). Sensitivity and specificity were significantly different between all groups except SRU versus TI-RADS. The Risk Threshold Algorithm, based on a malignancy risk model, demonstrates increased overall diagnostic accuracy as compared with ACR TI-RADS, SRU, and ATA management guidelines. Through eliminating unnecessary biopsy, patient anxiety, and morbidity can be reduced.
机译：我们研究了使用恶性风险模型的算法指导甲状腺结节管理的诊断性能，与现存管理指南相比。单机制，回顾性研究是从2012年至2015年的病理登记处进行的顺序案例进行。七十八名患者注册，患有愿望结果定义的良性和恶性群体。基于Logistic回归模型的风险阈值算法确定管理和风险阈值。美国放射学院的甲状腺影像学院，报告和数据系统（ACR TI-RADS），超声波（SRU）和美国甲状腺协会（ATA）指南中使用的放射科学家和ACR TI-RADS）。衍生灵敏度，特异性，正/否定预测值，接收器操作特征（ROC）值，通过McNemar和置换测试评估了重要性。包括四十四个良性结节和40个乳头状甲状腺癌。 ROC曲线下的风险阈值算法面积为0.80与0.59（ACR TI-RAD），0.49（SRU）和0.44（ATA）; ROC曲线差异下的所有区域都具有统计学意义。风险阈值算法表现出灵敏度，特异性，阳性预测值和负面预测值，在风险阈值下最大化诊断性能的风险阈值，而且为85％，39％，56％和74 ％（ACR TI-RADS）; 85％，18％，50％和57％（SRU）; 89％，11％，50％和83％（ATA）。除SRU与Ti-RAD之外的所有组之间的敏感性和特异性显着差异。与恶性风险模型的风险阈值算法表明，与ACR TI-RADS，SRU和ATA管理指南相比，增加了整体诊断准确性。通过消除不必要的活组织检查，患者焦虑和发病率可以减少。

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