Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE

Ruicheng LI; Hongwei QIAN; Yanni FAN; Peipei ZHAO; Shan WEI; Huarong JING

doi:10.12259/j.issn.2095-610X.S20241009

Volume 45 Issue 10

Oct. 2024

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Article Navigation > Journal of Kunming Medical University > 2024 > 45(10): 55-60

Ruicheng LI, Hongwei QIAN, Yanni FAN, Peipei ZHAO, Shan WEI, Huarong JING. Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE[J]. Journal of Kunming Medical University, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009

Citation:

Ruicheng LI, Hongwei QIAN, Yanni FAN, Peipei ZHAO, Shan WEI, Huarong JING. Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE[J]. Journal of Kunming Medical University, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009

Citation:

Ruicheng LI, Hongwei QIAN, Yanni FAN, Peipei ZHAO, Shan WEI, Huarong JING. Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE[J]. Journal of Kunming Medical University, 2024, 45(10): 55-60. doi: 10.12259/j.issn.2095-610X.S20241009

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Application of Logistic Regression and Artificial Neural Networks in the Differential Diagnosis of LC-MPE

doi: 10.12259/j.issn.2095-610X.S20241009

1.
Dept. of Clinical Laboratory
2.
Dept. of Information，The 2nd Affiliated Hospital of Air Force Medical University，Xi’an Shaanxi 710038，China

Received Date: 2024-05-20
Available Online: 2024-11-07
Publish Date: 2024-10-31

Abstract

Abstract

Objectives To evaluate the application value of carcinoembryonic antigen （CEA）, ferritin （FRT）, neuron-specific enolase （NSE）, squamous cell carcinoma-related antigen （SCC）, carbohydrate antigen 50 （CA50）, carbohydrate antigen 125 （CA125）, and cytokeratin 19 fragment （CY21-1） in serum （S-） and pleural effusion （P-） for differentiating malignant pleural effusion of lung cancer （LC-MPE） from benign pleural effusion （BPE）. We aim to establish a diagnostic model for LC-MPE using tumor markers and analyze the data using logistic regression and artificial neural network （ANN） techniques. Methods The serum and pleural effusion tumor marker results of patients with newly diagnosed LC-MPE and BPE were analyzed, and diagnostic models for LC-MPE were established using Logistic regression analysis and ANN technology. Results The indicators S-NSE, S-CY21-1, P-CEA, and P-NSE were selected and used for modeling. The Logistic regression model for diagnosing LC-MPE established in this study had a sensitivity of 93.23% and a specificity of 97.46%, with an area under the ROC curve of 0.992. The established ANN model had a sensitivity of 95.35%, a specificity of 97.22%, and an area under the ROC curve of 0.990 （P < 0.05）. Conclusions In diagnosing LC-MPE through tumor markers, both the Logistic regression model and the ANN model established in this study showed good diagnostic efficacy. These two models can assist clinicians in improving diagnostic accuracy.
- Pleural effusion,
- Artificial neural network,
- Tumor markers,
- Lung cancer,
- Diagnostic model

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References(22)

References

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