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. 2020 Mar 17;22(3):e17695.
doi: 10.2196/17695.

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data

Songjing Chen  1 , Sizhu Wu  1
Affiliations

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data

Songjing Chen et al. J Med Internet Res. .

Abstract

Background: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. With a rapid growth of the elderly population in recent years, lung cancer prevention and control are increasingly of fundamental importance, but are complicated by the fact that the pathogenesis of lung cancer is a complex process involving a variety of risk factors.

Objective: This study aimed at identifying key risk factors of lung cancer incidence in the elderly and quantitatively analyzing these risk factors' degree of influence using a deep learning method.

Methods: Based on Web-based survey data, we integrated multidisciplinary risk factors, including behavioral risk factors, disease history factors, environmental factors, and demographic factors, and then preprocessed these integrated data. We trained deep neural network models in a stratified elderly population. We then extracted risk factors of lung cancer in the elderly and conducted quantitative analyses of the degree of influence using the deep neural network models.

Results: The proposed model quantitatively identified risk factors based on 235,673 adults. The proposed deep neural network models of 4 groups (age ≥65 years, women ≥65 years old, men ≥65 years old, and the whole population) achieved good performance in identifying lung cancer risk factors, with accuracy ranging from 0.927 (95% CI 0.223-0.525; P=.002) to 0.962 (95% CI 0.530-0.751; P=.002) and the area under curve ranging from 0.913 (95% CI 0.564-0.803) to 0.931(95% CI 0.499-0.593). Smoking frequency was the leading risk factor for lung cancer in men 65 years and older. Time since quitting and smoking at least 100 cigarettes in their lifetime were the main risk factors for lung cancer in women 65 years and older. Men 65 years and older had the highest lung cancer incidence among the stratified groups, particularly non-small cell lung cancer incidence. Lung cancer incidence decreased more obviously in men than in women with smoking rate decline.

Conclusions: This study demonstrated a quantitative method to identify risk factors of lung cancer in the elderly. The proposed models provided intervention indicators to prevent lung cancer, especially in older men. This approach might be used as a risk factor identification tool to apply in other cancers and help physicians make decisions on cancer prevention.

Keywords: aged; deep learning; lung cancer; primary prevention; risk factors.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Data selection flowchart. BRFSS: Behavioral Risk Factor Surveillance System.
Figure 2
Figure 2
Schematic diagram of lung cancer risk factor identification in the elderly. DNN: deep neural network.
Figure 3
Figure 3
Deep learning model training process. DNN: deep neural network; HDF5: hierarchical data format version 5.
Figure 4
Figure 4
Data analysis equations.
Figure 5
Figure 5
Normalized weights of risk factors in the stratified groups. BMI: body mass index; CAT: computerized axial tomography; COPD: chronic obstructive pulmonary disease; CT: computed tomography; PM2.5: fine particulate matter with a diameter ≤2.5 μm.
Figure 6
Figure 6
Relationship between smoking and lung cancer incidence, 1996-2015.
See this image and copyright information in PMC

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