Wei Qiang, Fang Weizhen, Chen Xi, Yuan Zhongzhen, Du Yumei, Chang Yanbin, Wang Yonghong, Chen Shulin
Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Laboratory Medicine, Sun Yat-sen Memorial Hospital, Guangzhou, China.
Transl Lung Cancer Res. 2020 Oct;9(5):1843-1852. doi: 10.21037/tlcr-20-460.
In this study, we aimed to establish and validate a mathematical diagnosis model to distinguish benign pulmonary nodules (BPNs) from early non-small cell lung cancer (eNSCLC) based on clinical characteristics, radiomics features, and hematological biomarkers.
Medical records from 81 patients (27 BPNs, 54 eNSCLC) were used to establish a novel mathematical diagnosis model and an additional 61 patients (21 BPNs, 40 eNSCLC) were used to validate this new model. To establish a clinical diagnosis model, a least absolute shrinkage and selection operator (LASSO) regression was applied to select predictors for eNSCLC, then multivariate logistic regression analysis was performed to determine independent predictors of the probability of eNSCLC, and to establish a clinical diagnosis model. The diagnostic accuracy and discriminative ability of our model were compared with the PKUPH and Mayo models using the following 4 indices: area under the receiver-operating characteristics curve (ROC), net reclassification improvement index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA).
Multivariate logistic regression analysis identified age, border, and albumin (ALB) as independent diagnostic markers of eNSCLC. In the training cohort, the AUC of our model was 0.740, which was larger than the AUCs for the PKUPH model (0.717, P=0.755) and the Mayo model (0.652, P=0.275). Compared with the PKUPH and Mayo models, the NRI of our model increased by 3.7% (P=0.731) and 27.78% (P=0.008), respectively, while the IDI changed -4.77% (P=0.437) and 11.67% (P=0.015), respectively. Moreover, the DCA demonstrated that our model had a higher overall net benefit compared to previously published models. Importantly, similar findings were confirmed in the validation cohort.
Age, border, and serum ALB levels were independent diagnostic markers of eNSCLC. Thus, our model could more accurately distinguish BPNs from eNSCLC and outperformed previously published models.
在本研究中，我们旨在基于临床特征、影像组学特征和血液生物标志物建立并验证一个数学诊断模型，以区分良性肺结节（BPN）与早期非小细胞肺癌（eNSCLC）。
来自81例患者（27例BPN，54例eNSCLC）的病历用于建立一个新的数学诊断模型，另外61例患者（21例BPN，40例eNSCLC）用于验证该新模型。为建立临床诊断模型，应用最小绝对收缩和选择算子（LASSO）回归来选择eNSCLC的预测因子，然后进行多变量逻辑回归分析以确定eNSCLC概率的独立预测因子，并建立临床诊断模型。使用以下4个指标将我们模型的诊断准确性和判别能力与PKUPH模型和梅奥模型进行比较：受试者操作特征曲线（ROC）下面积、净重新分类改善指数（NRI）、综合判别改善指数（IDI）和决策曲线分析（DCA）。
多变量逻辑回归分析确定年龄、边界和白蛋白（ALB）为eNSCLC的独立诊断标志物。在训练队列中，我们模型的AUC为0.740，大于PKUPH模型的AUC（0.717，P = 0.755）和梅奥模型的AUC（0.652，P = 0.275）。与PKUPH模型和梅奥模型相比，我们模型的NRI分别增加了3.7%（P = 0.731）和27.78%（P = 0.008），而IDI分别变化了-4.77%（P = 0.437）和11.67%（P = 0.015）。此外，DCA表明我们的模型与先前发表的模型相比具有更高的总体净效益。重要的是，在验证队列中证实了类似的结果。
年龄、边界和血清ALB水平是eNSCLC的独立诊断标志物。因此，我们的模型可以更准确地区分BPN和eNSCLC，并且优于先前发表的模型。
