predicting survival in glioblastoma using gene expression databases: a neural network analysis

Glioblastoma (gbm) is the most aggressive and lethal brain tumor. artificial neural networks (anns) have the potential to make accurate predictions and improve decision making. the aim of this study was to create an ann model to predict 15-month survival in gbm patients according to gene expression databases. genomic data of gbm were downloaded from the cgga, tcga, myo, and cptac. logistic regression (lr) and ann model were used. age, gender, idh wild-type/mutant and the 31 most important genes from our previous study, were determined as input factors for the established ann model. 15-month survival time was used to evaluate the results. the normalized importance scores of each covariate were calculated using the selected ann model. the area under a receiver operating characteristic (roc) curve (auc), hosmer-lemeshow (h-l) statistic and accuracy of prediction were measured to evaluate the two models. spss 26 was utilized. a total of 551 patients (61% male, mean age 55.5 ± 13.3 years) patients were divided into training, testing, and validation datasets of 441, 55 and 55 patients, respectively. the main candidate genes found were: fn1, icam1, myd88, il10, and ccl2 with the ann model; and mmp9, myd88, and cdk4 with lr model. the aucs were 0.71 for the lr and 0.81 for the ann analysis. compared to the lr model, the ann model showed better results: accuracy rate, 83.3 %; h-l statistic, 6.5 %; and auc, 0.81 % of patients. the findings show that anns can accurately predict the 15-month survival in gbm patients and contribute to precise medical treatment.