Data from: Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules

Background: 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%. Methods: 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. Results: 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. Conclusions: 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. Trial Registry: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

背景：肺结节是临床诊断的一大难题，据估算美国每年新增约160万例肺结节患者。本研究针对癌症预测试概率（pretest probability of cancer, pCA）≤50%的患者，评估了整合蛋白质组分类器（integrated proteomic classifier）识别良性肺结节的准确性。
方法：本研究纳入685例直径8~30 mm的肺结节患者，开展一项前瞻性多中心观察性试验。采用多反应监测质谱法（multiple reaction monitoring mass spectrometry）检测两种血浆蛋白LG3BP与C163A的相对丰度，将检测结果与临床风险预测模型整合以识别疑似良性结节。计算敏感性、特异性与阴性预测值，并估算若采用该整合分类器结果指导临床决策时，侵入性检查的潜在变化情况。
结果：在178例经临床医师评估pCA≤50%的患者亚组中，肺癌患病率为16%。该整合蛋白质组分类器在区分良恶性肺结节时，敏感性达97%（置信区间confidence interval, CI：82%~100%），特异性为44%（置信区间36%~52%），阴性预测值为98%（置信区间92%~100%）。其性能优于正电子发射断层显像（PET）、已验证的肺结节风险模型以及医师的癌症概率评估（P<0.001）。若采用该整合分类器结果指导临床诊疗，良性结节的侵入性操作可减少40%，同时仅会有3%的恶性结节被误分类。
结论：针对pCA≤50%的肺结节患者，该整合蛋白质组分类器可准确识别良性肺结节，具备优异的性能表现。若应用于临床实践，通过将良性结节患者转入随访监测流程，可减少侵入性操作的实施。
试验注册：ClinicalTrials.gov；编号：NCT01752114；网址：www.clinicaltrials.gov。