Esophageal cancer is a high occult malignant tumor. Even with good diagnosis and treatment, the 5-year survival rate of esophageal cancer patients is still less than 30%. Considering the influence of clinical characteristics on postoperative esophageal cancer patients, the construction of a neural network model will help improve the poor prognosis of patients in the five years.
Material and methods
In this study, genetic algorithm optimized deep neural network is exploited to the clinical dataset of esophageal cancer. The independent prognostic factors are screened by Relief algorithm and Cox proportional risk regression. FTD prognostic staging system is established to assess the risk level of esophageal cancer patients.
FTD staging system and independent prognostic factors are integrated into the genetic algorithm optimized deep neural network. The Area Under Curve (AUC) of FTD staging system is 0.802. FTD staging system is verified by the Kaplan-Meier survival curve, and the median survival time is divided for different risk grades. The FTD staging system is superior to the TNM stages in the prognosis effect. The AUC of deep neural network optimized by genetic algorithm is 0.91.
The deep neural network optimized by genetic algorithm has good performance in predicting the 5-year survival status of esophageal cancer patients. The FTD staging system has a significant prognostic effect. The FTD staging system and genetic algorithm optimized deep neural network can be successfully availed in clinical diagnosis and treatment.Le texte complet de cet article est disponible en PDF.
Find the most suitable prediction model method: feature selection-deep learning.
Select the strong survival correlation feature set from Clinical dataset.
FTD system predicts the median survival time of patients at different stages.
GA optimized DNN to improve the model accuracy of esophageal cancer.
GA-DNN is successfully applied to a non-image continuous dataset.
Keywords : Genetic algorithm, Deep neural network, Esophageal cancer, Relief algorithm, Cox proportional risk regression analysis
|☆|| This work was supported in part by the Joint Funds of the National Natural Science Foundation of China under Grant U1804262, in part by the Foundation of Young Key Teachers from University of Henan Province under Grant 2018GGJS092, in part by the Thousand Talents Program under Grant 2018HYTP016, in part by the Henan Province University Science and Technology Innovation Talent Support Plan under Grant 20HASTIT027, in part by the Zhongyuan Thousand Talents Program under Grant 204200510003, and in part by the Open Fund of State Key Laboratory of Esophageal Cancer Prevention & Treatment under Grant K2020-0010 and Grant K2020-0011.