Diffuse large B-cell lymphoma (DLBCL) is a potentially curable disease, but standard treatment is not effective enough for all patients. That is why so important to identify high risk patients who need more aggressive therapy at the time of diagnosis. Nowadays prognosis for patients with DLBCL is based on International prognostic index (IPI). However, this index consists of only clinical parameters and does not include the biological characteristics of the tumour. Immunohistochemistry (IHC) markers could also play a prognostic role. There are some publications regarding predictive and prognostic role of expression of Bcl-2, Bcl-6, MUM1, CD10 and CD30, but their results are controversial. The aim of our study was to analyze prognostic role of these markers, to compare survival of patients with positive and negative expression of these markers and to build a prognostic model which include biological parameters for identifying high risk patients. There were statistically significant differences in EFS between the group of patients with negative and positive expression of CD10 (51.5 % versus 72.5 %, р=0.01) and in OS between the group of patients with negative and positive expression of Bcl-6 (61.1 % versus 79.6 %, р=0.03). Six-factors nonlinear neural network prediction model (MLP_6) was built. The sensitivity of the model is 63.2 % (95 % CІ 49.3 % – 75.6 %), specificity – 85.2 % (95 % CІ 79.1 % – 90.1 %). Prognostic factors include negative IHC expression of Bcl-6, CD10, non-GCB molecular subtype (according to algorithm Hans), gender (male), advanced Ann-Arbor stages, >2extranodal involvement. Our nonlinear neural network prediction model could improve prognostic role of IPI by adding of biological tumour characteristics (IHC expression of CD10, Bcl-6, molecular subtype by IHC algorithm).