Use of Neural Network in Predicting Gastric Cancer with Para-aortic Lymph Node Metastasis in a Hospital Population Within the Last Two Decades

Abstract

KEYWORDSgastric cancer, para-aortic lymph node metastasis, risk factors, neural network 2 Abstract Background: In clinical practice, the accurate prediction of para-aortic lymph node status and the selection of appropriate surgery methods can significantly affect the prognosis of patients with gastric cancer. In the present study, we reviewed the data of patients who underwent radical gastric cancer surgery with dissection of para-aortic lymph nodes (PANs) within the last 20 years and assessed the possible independent predictors of PAN status.Methods: We included 308 patients with gastric cancer who fulfilled the inclusion criteria, and logistic regression and neural network were utilized to identify the possible independent predictors of PAN status.Results: Logistic regression analysis showed that male sex, Borrmann types III and IV, T4 stage, and pancreatic or splenic metastases were significant risk factors of PAN metastasis after adjusting for other factors, and the accuracy rate was 71.8%. After inputting all parameters into the neural network, the accuracy was 98%.Conclusion: The neural network has significant benefits in predicting PAN status in patients with gastric cancer. The finding of this study may be useful in predicting PAN metastasis. Availability of data and materialThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.