Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
Barts and London, Wolfson Institute of Preventive Medicine, London, UK.
Thorax. 2019 Aug;74(8):761-767. doi: 10.1136/thoraxjnl-2018-212263. Epub 2019 Apr 26.
Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.
Data from the UK Lung Cancer Screening trial were analysed. Multivariable logistic regression models were used to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline and at 3-month and 12-month repeat screening.
Of 1994 participants who underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median follow-up of 4 years. Covariates that predict lung cancer in our model included female gender, asthma, bronchitis, asbestos exposure, history of cancer, early and late onset of family history of lung cancer, smoking duration, FVC, nodule type (pure ground-glass and part-solid) and volume as measured by semiautomated volumetry. The final model incorporating all predictors had excellent discrimination: area under the receiver operating characteristic curve (AUC 0.885, 95% CI 0.880 to 0.889). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected AUC 0.882, 95% CI 0.848 to 0.907). The risk model had a good calibration (goodness-of-fit χ[8] 8.13, p=0.42).
Our model may be used in estimating the probability of lung cancer in nodules detected at baseline and at 3 months and 12 months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programmes.
在肺结节患者中评估临床恶性肿瘤概率有助于早期诊断，确定最佳患者管理策略并降低总体成本。
对英国肺癌筛查试验的数据进行了分析。使用多变量逻辑回归模型来识别独立预测因子，并开发一个简约模型来估算基线时以及 3 个月和 12 个月重复筛查时检测到的肺结节中肺癌的概率。
在接受 CT 扫描的 1994 名参与者中，有 1013 名参与者总共出现 5063 个肺结节，其中 52 名（2.6%）参与者在中位数为 4 年的随访期间发生了肺癌。在我们的模型中，预测肺癌的协变量包括女性性别、哮喘、支气管炎、石棉暴露、癌症史、肺癌家族史的早发和晚发、吸烟持续时间、FVC、结节类型（纯磨玻璃和部分实性）以及半自动体积测量法测量的体积。纳入所有预测因子的最终模型具有出色的判别能力：接受者操作特征曲线下面积（AUC 0.885，95%CI 0.880 至 0.889）。内部验证表明，当应用于新数据时，该模型将具有良好的判别能力（校正后的 AUC 0.882，95%CI 0.848 至 0.907）。该风险模型具有良好的校准度（拟合优度 χ[8] 8.13，p=0.42）。
我们的模型可用于估算基线时以及 3 个月和 12 个月时检测到的结节中肺癌的概率，从而在基于人群的肺癌筛查计划中更有效地对随访进行分层。
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