PT -期刊文章AU -安东Schreuder盟-科林·雅各布斯AU -尼古拉斯Lessmann盟Mireille J.M. Broeders AU -马里奥席尔瓦盟伊凡娜Išgum AU - Pim a·德容盟尼古拉Sverzellati AU -马h神盟尤格Pastorino AU -科妮莉亚m . Schaefer-Prokop盟Bram van Ginneken TI -结合肺和心脏电脑断层生物标记特定疾病风险模型在肺癌筛查援助- 10.1183/13993003.03386 -2020 DP - 2021年9月01 TA -欧洲呼吸杂志》第六PG - 2003386 - 58 IP - 3 4099 - //www.qdcxjkg.com/content/58/3/2003386.short 4100 - //www.qdcxjkg.com/content/58/3/2003386.full所以欧元和J2021 9月01;58 AB -目标相结合的评估心血管疾病(CVD)、COPD和肺癌可能改善吸烟者的肺癌筛查的有效性。目的是获取和评估风险模型预测肺癌发病率,心血管疾病死亡率和慢性阻塞性肺病死亡率结合定量计算机断层扫描(CT)从每个疾病的措施,并量化自我报告的病人增加了预测的好处给CT扫描的可用性特征。方法调查模型(病人)特点,CT模型信息(CT)和最终模型(所有变量)派生为每个示例使用简洁的Cox回归结果从国家肺筛查试验(15 n = 000)。验证了使用多中心意大利肺检测数据(n = 2287)。时间模型歧视和校准报告的措施。结果年龄、平均肺密度,肺气肿,支气管壁厚和主动脉钙量是导致所有最终模型的变量。对肺结节的特征是至关重要的癌症发病率的预测但没有导致心血管疾病和慢性阻塞性肺病死亡率的预测。推导队列,肺癌发病率CT模型有一个5年接受者操作特征曲线下面积的82.5% (95% CI 80.9 - -84.0%),显著低于最终的模型(84.0%,-85.5% - 82.6)。然而,增加病人的特点没有提高肺癌发病率模型性能的验证队列(CT模型80.1%,74.2 - -86.0%;最终模型79.9%,73.9 - -85.8%)。 Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model 74.9%, 72.7–77.1%; CT model 76.3%, 74.1–78.5%; final model 79.1%, 77.0–81.2%), but not the validation cohort (survey model 74.8%, 62.2–87.5%; CT model 72.1%, 61.1–83.2%; final model 72.2%, 60.4–84.0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92.3%, 90.1–94.5%) compared to either other model individually (survey model 87.5%, 84.3–90.6%; CT model 87.9%, 84.8–91.0%), but no external validation was performed due to a very low event frequency.Conclusions CT measures of CVD and COPD provides small but reproducible improvements to nodule-based lung cancer risk prediction accuracy from 3 years onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.Quantitative computed tomography measures of cardiovascular disease and COPD may provide small but reproducible improvements to lung cancer risk prediction accuracy in a screening setting https://bit.ly/3sWyUMM