Thoracic Oncology Research Group Division of Pulmonary and Critical Care Medicine, Medical University of South Carolina, Charleston, SC.
Thoracic Oncology Research Group Division of Pulmonary and Critical Care Medicine, Medical University of South Carolina, Charleston, SC; Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Veterans Affairs Hospital, Charleston, SC.
Chest. 2018 Sep;154(3):491-500. doi: 10.1016/j.chest.2018.02.012. Epub 2018 Mar 1.
Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%.
A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made.
A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified.
When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance.
ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).
肺结节是一个诊断挑战，据估计，美国每年的发病率为 160 万例。本研究评估了一种综合蛋白质组分类器在识别术前癌症概率（pCA）≤50%的患者中的良性结节的准确性。
对 685 例 8-30mm 肺结节患者进行了前瞻性、多中心观察性试验。采用多重反应监测质谱法测量两种血浆蛋白 LG3BP 和 C163A 的相对丰度。结果与临床风险预测模型相结合，以识别可能的良性结节。计算了敏感性、特异性和阴性预测值。还估计了如果有综合分类器的结果并据此进行操作，将对侵入性检测产生的潜在变化。
在临床评估 pCA≤50%的 178 例患者亚组中，肺癌的患病率为 16%。综合分类器在区分良性和恶性结节方面显示出 97%（CI，82-100）的敏感性、44%（CI，36-52）的特异性和 98%（CI，92-100）的阴性预测值。该分类器的性能优于 PET、验证过的肺结节风险模型和医生癌症概率估计（P<0.001）。如果使用综合分类器的结果来指导治疗，良性结节的操作程序将减少 40%，而恶性结节将有 3%被误诊。
当用于术前癌症概率（pCA）≤50%的肺结节患者时，综合分类器可以准确识别良性肺结节，具有良好的性能特征。如果在临床实践中使用，通过将良性结节分流到监测中，可以减少侵入性程序。
ClinicalTrials.gov；编号：NCT01752114；网址：www.clinicaltrials.gov）。
