Artificial Neural Network as a Tool to Predict Severe Toxicity of Anticancer Drug Therapy in Patients with Gastric Cancer: A Retrospective Study.

Background. The aim of this study was to develop a predictive model of anticancer drug therapy toxicity in patients with gastric cancer. Methods. The retrospective study included 100 patients with stage II-IV gastric cancer who underwent 4 chemotherapy cycles. Initial significant toxicity factors included age, gender, height, body mass, body mass index, disease stage, skeletal muscle index (SMI), as well as plasma levels of trace elements (copper, zinc, selenium, manganese) and thyroid-stimulating hormone, cancer histology type and treatment regimen. The CTCAE v5.0 scale was employed to assess the severity of adverse events. Statistical analysis and building of mathematical neural network models were carried out in SPSS Statistics (v19.0). Results. Lower SMI values were associated with higher rates of toxicity-related complications of anticancer drug therapy (p < 0.05): leukopenia, hypoproteinemia, nausea, vomiting, cardiovascular events. Anemia, thrombocytopenia, hepatic cytolysis syndrome, nausea, diarrhea, constipation and stomatitis showed a weaker correlation with SMI. An increase in TSH was associated with higher rates of thrombocytopenia, nausea and vomiting. A decrease in Cu/Zn in plasma correlated with the severity of leukopenia and diarrhea, whereas Se/Mn showed an inverse correlation with the severity of anemia. Conclusions. Sarcopenia, abnormal thyroid status and imbalances in copper, zinc, selenium and manganese in blood plasma of patients with gastric cancer may be used as predictors of increased toxicity of anticancer drug therapy.