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首页> 外文期刊>Academic radiology >Use of Bayesian modeling to estimate the sensitivity of stereotactic directional vacuum-assisted breast biopsy when the gold standard is incomplete.
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Use of Bayesian modeling to estimate the sensitivity of stereotactic directional vacuum-assisted breast biopsy when the gold standard is incomplete.

机译：当金标准不完整时，使用贝叶斯模型估计立体定向定向真空辅助乳房活检的敏感性。

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RATIONALE AND OBJECTIVES: This study is conducted to estimate the sensitivity of stereotactic directional vacuum-assisted breast biopsy (ST DVAB) using Bayesian modeling and to predict how many more cancers can be inferred from those lesions without surgical correlation. MATERIALS AND METHODS: We retrospectively reviewed the 103 lesions from 84 women who underwent ST DVAB. The study was approved by the Institutional Review Board of our hospital. We estimated the sensitivity and prevalence of the study population for ST DVAB by two types of approaches: for the type I approach, the gold standards were surgical correlation or postbiopsy mammographic follow-up. For the type II approach using Bayesian modeling by a beta-binomial model, the only gold standard was surgical correlation and the predicted number of cancerous lesions in those patients without surgical correlation was estimated. RESULTS: For the type I approach, the sensitivity was 92.3%, and the prevalence 12.6%. For the type II approach, the mean sensitivity of ST DVAB was 89%, and the mean prevalence was 15%. We predicted that an average of 1.7 cancerous lesions occurred among those lesions without surgical correlation by the Bayesian estimation. CONCLUSIONS: The mean sensitivity of ST DVAB using the Bayesian (type II) approach was lower than that using the type I approach, because we regarded the surgery as the only gold standard in Bayesian modeling and the nonoperated lesions were thought to be with unknown true disease status. The Bayesian approach is thus more appropriate to use than the type I approach when the gold standard is incomplete.
机译：理由和目的：本研究旨在通过贝叶斯模型估计立体定向真空辅助乳腺活检（ST DVAB）的敏感性，并预测从这些病变中可以推断出多少癌症而无手术相关性。材料与方法：我们回顾性分析了84例行ST DVAB的女性的103处病变。该研究获得了我院机构审查委员会的批准。我们通过两种类型的方法评估了研究人群对ST DVAB的敏感性和患病率：对于I型方法，金标准是手术相关性或活检后的乳房X线照片随访。对于使用贝叶斯模型和β-二项式模型进行的II型方法，唯一的金标准是手术相关性，并且对那些没有手术相关性的患者进行了癌变病变的预测数目的估算。结果：对于I型方法，敏感性为92.3％，患病率为12.6％。对于II型方法，ST DVAB的平均敏感性为89％，平均患病率为15％。通过贝叶斯估计，我们预测这些病变中平均有1.7个癌性病变发生而无手术相关性。结论：使用贝叶斯（II型）方法的ST DVAB的平均敏感性低于使用I型方法，因为我们认为手术是贝叶斯建模中唯一的金标准，并且非手术性病变被认为具有未知的真实性。疾病状态。因此，当黄金标准不完整时，贝叶斯方法比I类方法更适合使用。

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