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温哥华肺癌风险预测模型:使用全国肺癌筛查试验队列子集进行评估。

The Vancouver Lung Cancer Risk Prediction Model: Assessment by Using a Subset of the National Lung Screening Trial Cohort.

机构信息

From the Department of Diagnostic Radiology, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (C.S.W.); Philips Healthcare, Highland Heights, Ohio (E.D.); and Philips Research North America, Briarcliff Manor, NY (E.C., L.B.).

出版信息

Radiology. 2017 Apr;283(1):264-272. doi: 10.1148/radiol.2016152627. Epub 2016 Oct 13.

Abstract

Purpose To assess the likelihood of malignancy among a subset of nodules in the National Lung Screening Trial (NLST) by using a risk calculator based on nodule and patient characteristics. Materials and Methods All authors received approval for use of NLST data. An institutional review board exemption and a waiver for informed consent were granted to the author with an academic appointment. Nodule characteristics and patient attributes with regard to benign and malignant nodules in the NLST were applied to a nodule risk calculator from a group in Vancouver, Canada. Patient populations and their nodule characteristics were compared between the NLST and Vancouver cohorts. Multiple thresholds were tested to distinguish benign nodules from malignant nodules. An optimized threshold value was used to determine positive and negative predictive values, and a full logistic regression model was applied to the NLST data set. Results Sufficient data were available for 4431 nodules (4315 benign nodules and 116 malignant nodules) from the NLST data set. The NLST and Vancouver data sets differed in that the former included fewer nodules per study, fewer nonsolid nodules, and more nodule spiculation and emphysema. A composite risk score threshold of 10% was determined to be optimal, demonstrating sensitivity, specificity, positive predictive value, and negative predictive value of 85.3%, 93.9%, 27.4%, and 99.6%, respectively. The receiver operating characteristic curve for the full regression model applied to the NLST database demonstrated an area under the receiver operating characteristic curve of 0.963 (95% confidence interval: 0.945, 0.974). Conclusion Application of an NLST data subset to the Vancouver risk calculator yielded a high discriminant value, which supports the use of a risk calculator method as a valuable approach to distinguish between benign and malignant nodules. RSNA, 2016 Online supplemental material is available for this article.

摘要

目的 通过使用基于结节和患者特征的风险计算器,评估国家肺癌筛查试验 (NLST) 中一部分结节的恶性可能性。

材料与方法 所有作者均获得 NLST 数据的使用批准。一位具有学术任命的作者获得了机构审查委员会豁免和知情同意豁免。NLST 中良性和恶性结节的结节特征和患者属性被应用于来自加拿大温哥华的一个小组的结节风险计算器。NLST 和温哥华队列之间比较了患者人群及其结节特征。测试了多个阈值来区分良性结节和恶性结节。使用优化的阈值来确定阳性和阴性预测值,并将全逻辑回归模型应用于 NLST 数据集。

结果 来自 NLST 数据集的 4431 个结节(4315 个良性结节和 116 个恶性结节)有足够的数据。NLST 和温哥华数据集之间存在差异,前者每个研究的结节数量较少,非实性结节较少,结节分叶和肺气肿较多。确定复合风险评分阈值为 10%是最佳的,分别显示敏感性、特异性、阳性预测值和阴性预测值为 85.3%、93.9%、27.4%和 99.6%。应用于 NLST 数据库的全回归模型的接收者操作特性曲线显示,接收者操作特性曲线下的面积为 0.963(95%置信区间:0.945,0.974)。

结论 将 NLST 数据子集应用于温哥华风险计算器产生了较高的判别值,这支持了风险计算器方法作为区分良性和恶性结节的一种有价值方法的使用。RSNA,2016 在线补充材料可用于本文。

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