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A predictive model to distinguish malignant and benign thyroid nodules based on age, gender and ultrasonographic features

机译:基于年龄,性别和超声特征的基于年龄,性别和超声特征来区分恶性和良性甲状腺结节的预测模型

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Introduction: A discussion in literature about a standardized decision support tool for the management of thyroid nodules remains. Objective: The purpose of this study was to create a statistical prediction model for thyroid nodules management. Methods: Two hundred and four benign and 57 malignant thyroid nodules were selected for a retrospective study. The variables age, gender and ultrasonographic features were examined using univariate and multivariate models. A statistical formula was used to calculate the risk of cancer of each case. Results: In multivariate analysis, irregular shape, absence of halo, lower mean age, homogeneous echotexture, microcalcifications and solid content were associated with cancer. After applying the formula, 20 cases (7.6%) with a calculated risk for malignancy ≤3.0% were found, all of them benign. Setting the calculated risk in ≥80%, 21 (8.0%) cases were selected, and in 85.7% of them cancer was confirmed in histopathology. Internal accuracy of the prediction formula was 92.5%. Conclusions: The prediction formula reached high accuracy and may be an alternative to other decision support tools for thyroid nodule management.
机译:简介:关于甲状腺结节管理的标准决策支持工具的文献仍然存在。目的:本研究的目的是为甲状腺结节管理创建统计预测模型。方法:选择两百九次良性和57个恶性甲状腺结节进行回顾性研究。使用单变量和多变量模型检查变量年龄,性别和超声波特征。使用统计公式来计算每种情况的癌症的风险。结果:在多变量分析,不规则形状,卤素的不存在,较低的平均年龄,均匀的呼吸眼,微钙化和固体含量与癌症有关。在涂上配方后,发现了20例(7.6%),发现了≤3.0%的计算风险,所有这些都是良性的。选择计算的风险≥80%,21例(8.0%)病例,在85.7%的癌症中,在组织病理学中证实了癌症。预测公式的内部精度为92.5%。结论:预测公式达到了高精度,可以是甲状腺结节管理的其他决策支持工具的替代品。

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