循环遗传异常细胞对支持 CT 诊断良、恶性肺结节的价值。
Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules.
机构信息
Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
General Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
出版信息
BMC Cancer. 2022 Apr 9;22(1):382. doi: 10.1186/s12885-022-09472-w.
BACKGROUND
The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening.
METHODS
From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21-1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve.
RESULTS
A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS.
CONCLUSIONS
CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.
背景
CT 和肿瘤标志物在肺癌筛查中的准确性有待提高。计算机辅助诊断已被报道可有效提高影像学数据的诊断准确性，最近的研究表明，循环中遗传异常细胞（CAC）有可能成为一种新的肺癌标志物。本研究旨在探索肺癌筛查的新方法。
方法
本研究纳入了 2020 年 5 月至 2021 年 4 月在郑州大学第一附属医院接受 CAC 检查且在手术或活检前一周内接受 CT 扫描的肺部结节患者。收集患者的 CAC 计数、CT 扫描图像、血清肿瘤标志物（CEA、CYFRA21-1、NSE）水平和人口统计学特征进行分析。将 CT 上传至肺部结节人工智能诊断系统（PNAIDS），以评估结节的恶性概率。我们比较了基于 PNAIDS、CAC、Mayo 诊所模型、肿瘤标志物单独及联合诊断的结果。通过逻辑回归建立联合模型，并通过 ROC 曲线下面积（AUC）进行比较。
结果
共纳入 111 例患者中的 93 例。PNAIDS 的 AUC 为 0.696，与 CAC 联合后增加至 0.847。联合模型的灵敏度（SE）、特异度（SP）、阳性（PPV）和阴性（NPV）预测值分别为 61.0%、94.1%、94.7%和 58.2%。此外，我们评估了 CAC 的诊断价值，其 AUC 为 0.779，SE 为 76.3%，SP 为 64.7%，PPV 为 78.9%，NPV 为 61.1%，均高于任何单个血清肿瘤标志物和 Mayo 诊所模型。PNAIDS 和 CAC 的联合应用的 AUC 值明显高于 PNAIDS（P=0.009）或 CAC（P=0.047）指标单独应用。然而，加入额外的肿瘤标志物并没有显著改变 CAC 和 PNAIDS 的性能。
结论
CAC 在早期肺癌中的诊断价值高于传统肿瘤标志物，并对基于肺部结节的 PNAIDS 癌症诊断具有辅助价值。本研究结果为使用肺部结节进行早期肺癌筛查提供了一种新模式。
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