A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules,Advanced Science

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A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules
Advanced Science ( IF 14.3 ) Pub Date : 2021-05-07 , DOI: 10.1002/advs.202100104
Quan-Xing Liu ₁ , Dong Zhou ₁ , Tian-Cheng Han ₂ , Xiao Lu ₁ , Bing Hou ₁ , Man-Yuan Li ₁ , Gui-Xue Yang ₁ , Qing-Yuan Li ₂ , Zhi-Hua Pei ₂ , Yuan-Yuan Hong ₂ , Ya-Xi Zhang ₂ , Wei-Zhi Chen ₂ , Hong Zheng ₁ , Ji He ₂ , Ji-Gang Dai ₁

Affiliation

Addressing the high false-positive rate of conventional low-dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood-based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing- (NGS-) based cell-free DNA (cfDNA) mutation profiling, NGS-based cfDNA methylation profiling, and blood-based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high-risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98-patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29-patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%.

中文翻译：

肺结节患者肺癌诊断的无创多重分析方法

针对传统低剂量计算机断层扫描 (LDCT) 诊断肺癌的假阳性率较高的问题，研究了结合血液无创检测来协助执业临床医生诊断肺结节 (PN) 决策的功效。在这项前瞻性观察性研究中，对诊断为 PN 的患者进行了基于下一代测序 (NGS) 的无细胞 DNA (cfDNA) 突变分析、基于 NGS 的 cfDNA 甲基化分析和基于血液的蛋白质癌症生物标志物检测通过LDCT将其列为高危患者，随后进行手术切除，并对组织切片进行病理检查和分类。以病理分类为金标准，基于98例患者发现队列（28例良性和70例恶性），采用统计和机器学习方法选择与组织恶性分类相关的分子标志物，并构建组织恶性肿瘤的综合多分析模型预言。基于各个测试平台的预测模型显示出不同的性能水平，而最终的综合模型产生的受试者工作特征曲线 (AUC) 下的面积为 0.85。该模型的性能在 29 名患者的独立验证队列（14 名良性患者和 15 名恶性患者，功效 > 0.90）上得到进一步证实，再现 AUC 为 0.86，这意味着总体敏感性为 80%，特异性为 85.7%。

更新日期：2021-07-07

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