Background. Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs). Methods. The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (). Results. The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone. Conclusion. Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.

中文翻译：

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七种自身抗体panel联合Mayo模型在肺结节鉴别诊断中的价值

背景。识别恶性肺结节和检测早期肺癌 (LC) 可以降低死亡率。本研究调查了七种自身抗体 (7-AAB) 组合与 Mayo 模型相结合的临床价值，用于早期检测 LC 和区分良恶性肺结节 (MPN)。方法。通过酶联免疫吸附试验 (ELISA) 在 806 名参与者中对 7-AAB 面板的元素浓度进行了定量。使用 Mayo 预测模型计算 MPN 的概率。7-AAB 面板和 Mayo 模型的性能通过接受者操作特征 (ROC) 分析进行分析，并通过卡方检验评估组间差异 ( )。结果 . 所有 7 个 AAB 的 ROC 曲线下面积 (AUC) 均高于单个 AAB。7-AAB 组的敏感性在 I-II 期 LC 患者中为 67.5%，在 III-IV 期患者中为 60.3%，健康对照组的特异性为 89.6%，良性肺病患者的特异性为 83.1%。7-AAB panel 在早期 LC 患者中的检出率高于传统肿瘤标志物。在区分 MPN 和良性结节方面，7-AAB 组与 Mayo 模型的 AUC 高于单独的 7-AAB 组或单独的 Mayo 模型。对于早期 MPN，该组合的敏感性和特异性分别为 93.5% 和 58.0%。对于晚期 MPN，该组合的敏感性和特异性分别为 91.4% 和 72.8%。结论。我们的研究证实了 7-AAB panel 对肺癌早期检测的临床价值，并结合 Mayo 模型可用于区分良恶性肺结节。