Section of Radiology (Pad. Barbieri), Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, University of Parma, Via Gramsci 14, 43126, Parma, Italy.
Department of Thoracic Surgery, IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Eur Radiol. 2021 Apr;31(4):1956-1968. doi: 10.1007/s00330-020-07275-w. Epub 2020 Sep 30.
The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS).
Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden.
In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6-21.9% and a potential reduction of LDCT burden of 25.5-41%.
Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible.
• Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5-41% reduction of LDCT burden, at the cost of 13.6-21.9% rate of delayed diagnosis.
2019 年肺部 CT 筛查报告和数据系统第 1.1 版(Lung-RADS v1.1)引入了用于结节管理的体积类别。本研究的目的是报告 Lung-RADS v1.1 体积类别的分布,并分析 3 年内肺癌(LC)的结果,以探索个性化的肺癌筛查(LCS)算法。
通过国家肺癌筛查试验(NLST)标准,从多中心意大利肺部检测(MILD)试验中回顾性选择受试者。基线特征包括根据 Lung-RADS v1.1 的基于体积的类别选择的测试前指标和结节特征。结节体积通过专用半自动软件进行分割获得。主要结局是 LC 的诊断,通过单变量和多变量模型进行测试。次要结局是 LC 的分期。模拟了增加间隔的算法,以测试延迟诊断(RDD)的发生率和降低低剂量计算机断层扫描(LDCT)的负担。
在 1248 名符合 NLST 条件的受试者中,1 年时 LC 频率为 1.2%,2 年时为 1.8%,3 年时为 2.6%。Lung-RADS v1.1 中的结节体积是 LC 的强烈预测因子:阳性 LDCT 在 1 年时的优势比(OR)为 75.60(p<0.0001),而不确定的 LDCT 在 2 年时的 OR 为 9.16(p=0.0068),在 3 年时的 OR 为 6.35(p=0.0042)。在阴性 LDCT 后的前 2 年内,100%切除的 LC 分期为 I 期。低频筛查的模拟显示 RDD 为 13.6-21.9%,LDCT 负担潜在减少 25.5-41%。
半自动软件的结节体积允许在 Lung-RADS v1.1 类别中对 LC 风险进行分层。通过增加间隔的个性化筛查算法在 80%的 NLST 合格者中似乎是可行的。
使用结节体积的半自动分割,Lung-RADS v1.1 选择了 10.8%的 CT 阳性患者,与 CT 阴性相比,1 年时肺癌的相对风险为 96.87。
阴性的 Lung-RADS v1.1 低剂量 CT 在 80.6%的 NLST 合格者中发现,与阳性的低剂量 CT 相比,2 年内肺癌的相对风险降低了 40 倍;在该组中,年度筛查可能更敏感。
基于体积的 Lung-RADS v1.1 的结节体积半自动分割和增加的筛查间隔可能会回溯性地建议降低 25.5-41%的 LDCT 负担,代价是 13.6-21.9%的延迟诊断率。