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Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

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Abstract

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569–0.761) and 0.760 (0.664–0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China (NSFC 81573203, 30972457, 81001239) and the Outstanding Youth Grant of Zhengzhou University (No. 1421329082).

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    Authors and Affiliations

    1. Department of Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China

      Xiaoran Duan & Liuxin Cui

    2. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China

      Yongli Yang

    3. Department of Hospital Infection Management, Qingdao Municipal Hospital, Qingdao, China

      Shanjuan Tan

    4. Department of Occupational Health, Henan Institute of Occupational Health, Zhengzhou, China

      Sihua Wang

    5. Department of Occupational Health and Occupational Medicine, College of Public Health, Zhengzhou University, Zhengzhou, China

      Xiaolei Feng & Wei Wang

    6. Department of Health Toxicology, College of Public Health, Zhengzhou University, Zhengzhou, China

      Feifei Feng & Yongjun Wu

    7. Department of Sanitary Chemistry, College of Public Health, Zhengzhou University, Zhengzhou, China

      Songcheng Yu

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    1. Xiaoran Duan
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    Correspondence to Wei Wang or Yongjun Wu.

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    Xiaoran Duan and Yongli Yang have contributed equally to this work.

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    Duan, X., Yang, Y., Tan, S. et al. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer. Med Biol Eng Comput 55, 1239–1248 (2017). https://doi.org/10.1007/s11517-016-1585-7

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    • DOI: https://doi.org/10.1007/s11517-016-1585-7

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