Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China.
Department of Molecular Pathology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, No 127, Dongming Road, Zhengzhou, 450008, Henan, China.
BMC Cancer. 2021 Mar 10;21(1):263. doi: 10.1186/s12885-021-08002-4.
Lung cancer remains the leading cause of cancer deaths across the world. Early detection of lung cancer by low-dose computed tomography (LDCT) can reduce the mortality rate. However, making a definitive preoperative diagnosis of malignant pulmonary nodules (PNs) found by LDCT is a clinical challenge. This study aimed to develop a prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant pulmonary nodules from benign PNs.
We assessed three DNA methylation biomarkers (PTGER4, RASSF1A, and SHOX2) and clinically-relevant variables in a training cohort of 110 individuals with PNs. Four machine-learning-based prediction models were established and compared, including the K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and logistic regression (LR) algorithms. Variables of the best-performing algorithm (LR) were selected through stepwise use of Akaike's information criterion (AIC). The constructed prediction model was compared with the methylation biomarkers and the Mayo Clinic model using the non-parametric approach of DeLong et al. with the area under a receiver operator characteristic curve (AUC) analysis.
A prediction model was finally constructed based on three DNA methylation biomarkers and one radiological characteristic for identifying malignant from benign PNs. The developed prediction model achieved an AUC value of 0.951 in malignant PNs diagnosis, significantly higher than the three DNA methylation biomarkers (0.912, 95% CI:0.843-0.958, p = 0.013) or Mayo Clinic model (0.823, 95% CI:0.739-0.890, p = 0.001). Validation of the prediction model in the testing cohort of 100 subjects with PNs confirmed the diagnostic value.
We have shown that integrating DNA methylation biomarkers and radiological characteristics could more accurately identify lung cancer in subjects with CT-found PNs. The prediction model developed in our study may provide clinical utility in combination with LDCT to improve the over-all diagnosis of lung cancer.
肺癌仍然是全球癌症死亡的主要原因。通过低剂量计算机断层扫描（LDCT）早期发现肺癌可以降低死亡率。然而，对 LDCT 发现的恶性肺结节（PN）做出明确的术前诊断是一个临床挑战。本研究旨在开发一种基于 DNA 甲基化生物标志物和影像学特征的预测模型，以识别良恶性肺结节。
我们评估了 110 名患有肺结节的个体的三个 DNA 甲基化生物标志物（PTGER4、RASSF1A 和 SHOX2）和临床相关变量。建立并比较了四种基于机器学习的预测模型，包括 K-最近邻（KNN）、随机森林（RF）、支持向量机（SVM）和逻辑回归（LR）算法。通过使用 Akaike 信息准则（AIC）的逐步方法选择最佳性能算法（LR）的变量。使用 DeLong 等人的非参数方法比较构建的预测模型与甲基化生物标志物和 Mayo 诊所模型，通过接收者操作特征曲线（AUC）分析评估曲线下面积（AUC）。
最终构建了一个基于三个 DNA 甲基化生物标志物和一个影像学特征的预测模型，用于识别良恶性肺结节。该预测模型在恶性肺结节诊断中的 AUC 值为 0.951，明显高于三个 DNA 甲基化生物标志物（0.912，95%CI：0.843-0.958，p=0.013）或 Mayo 诊所模型（0.823，95%CI：0.739-0.890，p=0.001）。在 100 名肺结节患者的测试队列中验证预测模型，确认了该模型的诊断价值。
我们表明，整合 DNA 甲基化生物标志物和影像学特征可以更准确地识别 CT 发现的肺结节中的肺癌。我们研究中开发的预测模型与 LDCT 结合可能具有临床应用价值，以提高肺癌的整体诊断率。
