Suppr超能文献

CT 检测到肺结节患者的肺癌概率:来自低剂量 CT 筛查 NELSON 试验数据的预设分析。

Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.

机构信息

Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands; Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, Netherlands.

Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands; Department of Biostatistics, Erasmus University Medical Center, Rotterdam, Netherlands.

出版信息

Lancet Oncol. 2014 Nov;15(12):1332-41. doi: 10.1016/S1470-2045(14)70389-4. Epub 2014 Oct 1.

Abstract

BACKGROUND

The main challenge in CT screening for lung cancer is the high prevalence of pulmonary nodules and the relatively low incidence of lung cancer. Management protocols use thresholds for nodule size and growth rate to determine which nodules require additional diagnostic procedures, but these should be based on individuals' probabilities of developing lung cancer. In this prespecified analysis, using data from the NELSON CT screening trial, we aimed to quantify how nodule diameter, volume, and volume doubling time affect the probability of developing lung cancer within 2 years of a CT scan, and to propose and evaluate thresholds for management protocols.

METHODS

Eligible participants in the NELSON trial were those aged 50-75 years, who have smoked 15 cigarettes or more per day for more than 25 years, or ten cigarettes or more for more than 30 years and were still smoking, or had stopped smoking less than 10 years ago. Participants were randomly assigned to low-dose CT screening at increasing intervals, or no screening. We included all participants assigned to the screening group who had attended at least one round of screening, and whose results were available from the national cancer registry database. We calculated lung cancer probabilities, stratified by nodule diameter, volume, and volume doubling time and did logistic regression analysis using diameter, volume, volume doubling time, and multinodularity as potential predictor variables. We assessed management strategies based on nodule threshold characteristics for specificity and sensitivity, and compared them to the American College of Chest Physicians (ACCP) guidelines. The NELSON trial is registered at www.trialregister.nl, number ISRCTN63545820.

FINDINGS

Volume, volume doubling time, and volumetry-based diameter of 9681 non-calcified nodules detected by CT screening in 7155 participants in the screening group of NELSON were used to quantify lung cancer probability. Lung cancer probability was low in participants with a nodule volume of 100 mm(3) or smaller (0·6% [95% CI 0·4-0·8]) or maximum transverse diameter smaller than 5 mm (0·4% [0·2-0·7]), and not significantly different from participants without nodules (0·4% [0·3-0·6], p=0·17 and p=1·00, respectively). Lung cancer probability was intermediate (requiring follow-up CT) if nodules had a volume of 100-300 mm(3) (2·4% [95% CI 1·7-3·5]) or a diameter 5-10 mm (1·3% [1·0-1·8]). Volume doubling time further stratified the probabilities: 0·8% (95% CI 0·4-1·7) for volume doubling times 600 days or more, 4·0% (1·8-8·3) for volume doubling times 400-600 days, and 9·9% (6·9-14·1) for volume doubling times of 400 days or fewer. Lung cancer probability was high for participants with nodule volumes 300 mm(3) or bigger (16·9% [95% CI 14·1-20·0]) or diameters 10 mm or bigger (15·2% [12·7-18·1]). The simulated ACCP management protocol yielded a sensitivity and specificity of 90·9% (95% CI 81·2-96·1), and 87·2% (86·4-87·9), respectively. A diameter-based protocol with volumetry-based nodule diameter yielded a higher sensitivity (92·4% [95% CI 83·1-97·1]), and a higher specificity (90·0% [89·3-90·7). A volume-based protocol (with thresholds based on lung cancer probability) yielded the same sensitivity as the ACCP protocol (90·9% [95% CI 81·2-96·1]), and a higher specificity (94·9% [94·4-95·4]).

INTERPRETATION

Small nodules (those with a volume <100 mm(3) or diameter <5 mm) are not predictive for lung cancer. Immediate diagnostic evaluation is necessary for large nodules (≥300 mm(3) or ≥10 mm). Volume doubling time assessment is advocated only for intermediate-sized nodules (with a volume ranging between 100-300 mm(3) or diameter of 5-10 mm). Nodule management protocols based on these thresholds performed better than the simulated ACCP nodule protocol.

FUNDING

Zorgonderzoek Nederland Medische Wetenschappen and Koningin Wilhelmina Fonds.

摘要

背景

在肺癌 CT 筛查中,主要的挑战是肺结节的高患病率和肺癌的相对低发病率。管理方案使用结节大小和生长速度的阈值来确定哪些结节需要额外的诊断程序,但这些阈值应该基于个体患肺癌的概率。在这项预设分析中,我们使用 NELSON CT 筛查试验的数据,旨在定量研究结节直径、体积和倍增时间如何影响 CT 扫描后 2 年内患肺癌的概率,并提出和评估管理方案的阈值。

方法

NELSON 试验中符合条件的参与者为年龄在 50-75 岁之间、每天吸烟 15 支或以上且吸烟超过 25 年、或每天吸烟 10 支或以上且吸烟超过 30 年但仍在吸烟、或戒烟时间不足 10 年的人群。参与者被随机分配到低剂量 CT 筛查组或不筛查组。我们纳入了所有被分配到筛查组且至少参加了一轮筛查、且结果可从国家癌症登记数据库获得的参与者。我们根据结节直径、体积和倍增时间,计算了肺癌的概率,并按结节直径、体积和倍增时间进行分层,使用直径、体积、倍增时间和多灶性作为潜在的预测变量进行 logistic 回归分析。我们根据结节阈值特征评估了管理策略的特异性和敏感性,并将其与美国胸科医师学会(ACCP)指南进行了比较。NELSON 试验在 www.trialregister.nl 注册,编号为 ISRCTN63545820。

结果

我们使用 NELSON 试验中筛查组 7155 名参与者的 9681 个非钙化结节的 CT 筛查结果的体积、倍增时间和基于体积的直径来量化肺癌的概率。体积 100mm³或更小(0.6%[95%CI 0.4-0.8])或最大横径小于 5mm(0.4%[0.2-0.7])的参与者患肺癌的概率较低,与无结节的参与者无显著差异(0.4%[0.3-0.6],p=0.17 和 p=1.00)。如果结节体积为 100-300mm³(2.4%[95%CI 1.7-3.5])或直径为 5-10mm(1.3%[1.0-1.8]),则肺癌的概率为中等(需要进行随访 CT)。倍增时间进一步分层了概率:倍增时间为 600 天或更长的为 0.8%(95%CI 0.4-1.7),倍增时间为 400-600 天的为 4.0%(1.8-8.3),倍增时间为 400 天或更短的为 9.9%(6.9-14.1)。体积为 300mm³或更大(16.9%[95%CI 14.1-20.0])或直径为 10mm 或更大(15.2%[12.7-18.1])的参与者患肺癌的概率较高。模拟的 ACCP 管理方案的敏感性和特异性分别为 90.9%(95%CI 81.2-96.1)和 87.2%(86.4-87.9)。基于体积的方案(基于体积的结节直径)具有更高的敏感性(92.4%[95%CI 83.1-97.1])和特异性(90.0%[89.3-90.7])。基于体积的方案(基于肺癌概率的阈值)与 ACCP 方案具有相同的敏感性(90.9%[95%CI 81.2-96.1]),但特异性更高(94.9%[94.4-95.4])。

结论

小结节(体积<100mm³或直径<5mm)不具有预测肺癌的能力。对于大结节(≥300mm³或≥10mm),需要进行直接的诊断评估。仅对中等大小的结节(体积在 100-300mm³或直径在 5-10mm 之间)进行倍增时间评估。基于这些阈值的结节管理方案比模拟的 ACCP 结节方案表现更好。

资助

荷兰医疗科学研究组织和荷兰女王威廉明娜基金。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验