Application of artificial neural network in evaluation of the bone marrow depression following high-dose methotrexate chemotherapy

OBJECTIVE To establish an artificial neural network(ANN) to evaluate the bone marrow depression following high-dose methotrexate(HDMTX) chemotherapy, and to facilitate individualized therapeutic regimens. METHODS Data obtained from 180 cases of children with acute lymphoblastic leukemia during HDMTX treatment were divided into two groups randomly, as training group(n=150) and testing group(n=30). The decrease percent in NEU count post-HDMTX infusion was selected as the ANN output, which was the prediction marker of bone marrow depression. ANN was established after the network parameters were trained by using momentum method combined with genetic algorithm based on the training group data. The decrease percent in NEU count of testing group patients were predicted by ANN established, and the mean predicted error(MPE), weighted residuals (WRES), mean absolute prediction error(MAE), mean squared prediction error(MSE), root mean squared prediction error (RMSE) were calculated to assess the ANN model. RESULTS The assessed results of ANN were MPE (-2.05±7.41)%, WRES (23.20±29.74)%, MAE (6.12±4.53)%,MSE (57.26±64.46)(%)², RMSE 7.57%, respectively. There were 76.67% of relative prediction error within ±20%. The accuracy and precision of ANN were superior to those of multiple linear regression with stepwise method. CONCLUSION The performance of ANN established in this study is good enough to predict the degree of bone marrow depression following HDMTX chemotherapy and optimize individualized saving regimens.