Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers
Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers
复制标题
DOI：
10.1177/15353702231177013
复制
发表时间：
2023-05
影响因子：
3.2
通讯作者：
Yanan Luo;Hui Yuan;Qin Pei;Yiyu Chen;Jiawen Xian;Rongrong Du;Ting Ye
中科院分区：
文献类型：
--
作者：
Yanan Luo;Hui Yuan;Qin Pei;Yiyu Chen;Jiawen Xian;Rongrong Du;Ting Ye
本研究旨在建立常规实验室指标结合肿瘤标志物的肺癌诊断预测模型，以方便、快速、廉价的方式帮助肺癌的早期筛查和辅助诊断，提高肺癌的早期诊断率。回顾性研究221例肺癌患者、100例肺部良性疾病患者和184例健康受试者。收集一般临床资料、常规实验室指标及肿瘤标志物结果。使用统计产品和服务解决方案26.0进行数据分析。采用人工神经网络-多层感知器建立了肺癌的诊断预测模型。5个对照组（肺癌-良性肺病组、肺癌-健康组、良性肺病-健康组、早期肺癌-良性肺病组、早期肺癌-健康组）通过相关分析和差异分析，分别得到5、28、25、16、25个预测肺癌或良性肺病的有价值指标，并分别建立5种诊断预测模型。各联合诊断预测模型的曲线下面积（AUC）（0.848、0.989、0.949、0.841、0.976）均高于单纯利用肿瘤标志物建立的诊断预测模型（0.799、0.941、0.830、0.661、0.850），且肺癌健康组、良性肺病健康组、早期肺癌良性肺病组、早期肺癌健康组间差异均有统计学意义（P < 0.05）。基于人工神经网络的肺癌诊断模型将常规指标与肿瘤标志物相结合，在辅助早期肺癌诊断方面具有较高的性能和临床意义。
This study set out to establish a lung cancer diagnosis and prediction model uses conventional laboratory indicators combined with tumor markers, so as to help early screening and auxiliary diagnosis of lung cancer through a convenient, fast, and cheap way, and improve the early diagnosis rate of lung cancer. A total of 221 patients with lung cancer, 100 patients with benign pulmonary diseases, and 184 healthy subjects were retrospectively studied. General clinical data, the results of conventional laboratory indicators, and tumor markers were collected. Statistical Product and Service Solutions 26.0 was used for data analysis. The diagnosis and prediction model of lung cancer was established by artificial neural network – multilayer perceptron. After correlation and difference analysis, five comparison groups (lung cancer-benign lung disease group, lung cancer-health group, benign lung disease-health group, early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group) obtained 5, 28, 25, 16, and 25 valuable indicators for predicting lung cancer or benign lung disease, and then established five diagnostic prediction models, respectively. The area under the curve (AUC) of each combined diagnostic prediction model (0.848, 0.989, 0.949, 0.841, and 0.976) was higher than that of the diagnostic prediction model established only using tumor markers (0.799, 0.941, 0.830, 0.661, and 0.850), and the difference in the lung cancer-health group, the benign lung disease-health group, the early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group was statistically significant (P < 0.05). The artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers have high performance and clinical significance in assisting the diagnosis of early lung cancer.