Machine-Learning Approach for Modeling Myelosuppression Attributed to Nimustine Hydrochloride.

Abstract

A major adverse effect arising from nimustine hydrochloride (ACNU) therapy for brain tumors is myelosuppression. Because its timing and severity vary among individual patients, the ACNU dose level has been adjusted in an empiric manner at individual medical facilities. To our knowledge, ours is the first study to develop a machine-learning approach to estimate myelosuppression through analysis of patient factors before treatment and attempts to clarify the relationship between myelosuppression and hematopoietic stem cells from daily clinical data. Adverse effect prediction will allow ACNU dose adjustment for patients predicted to have decreases in blood cell counts and will enable focused follow-up of patients undergoing chemoradiotherapy. Patients were newly pathologically diagnosed with WHO grade 2 or 3 tumors and were treated with ACNU-based chemoradiotherapy. For detailed analysis of the timing and intensity of adverse effects in patients, we developed a data-weighted support vector machine (SVM) based on adverse event criteria (nadir-weighted SVM [NwSVM]). To evaluate the estimation accuracy of blood cell count dynamics, the determination coefficient ( r²) between real and estimated data was calculated by three regression methods: polynomial, SVM, and NwSVM. Only the NwSVM-based regression enabled estimation of the dynamics of all blood cell types with high accuracy (mean r² = 0.81). The mean timing of nadir arrival estimated using this regression was 35 days for platelets, 41 days for RBCs, 52 days for lymphocytes, 57 days for WBCs, and 62 days for neutrophils. The NwSVM can be used to predict myelosuppression and clearly depicts nadir timing differences between platelets and other blood cells.