Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Author:

Luo Yanan1,Yuan Hui2,Pei Qin1,Chen Yiyu1,Xian Jiawen1,Du Rongrong1,Ye Ting1ORCID

Affiliation:

1. Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, P.R. China

2. Department of Pathophysiology, Mudanjiang Medical University, Mudanjiang 157011, P.R. China

Abstract

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.

Funder

the National Natural Science Youth Fund, China

Medical Science and Technology Project of Sichuan Provincial Health Commission, China

the Sichuan Science and Technology Program for key Research and Development, China

Doctoral Research Initiation Fund of Affiliated Hospital of Southwest Medical University, China

Publisher

Frontiers Media SA

Subject

General Biochemistry, Genetics and Molecular Biology

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