Neuroblastoma is a childhood cancer that affects thousands of kids worldwide every years. Electronic health records (EHRs) of neuroblastoma patients contain valuable data for physicians and researchers because they collect both well-known clinical factors and factors whose clinical value has never been investigated. In this study, we analyzed data from EHRs of 3,034 patients with neuroblastoma from the Registro Italiano dei Tumori Neuroblastici Periferici. To perform the analysis we applied a supervised machine learning approach based on Random Forests for predicting patients’ outcome and relapse/progression, and a recursive feature elimination (RFE) approach for feature ranking. Feature ranking indicated the time to maximum response to first-line treatment in addition to the maximum response to first-line treatment as one of the most predictive factors of patient outcome, thus providing to physicians new potential treatment indications for patients affected by neuroblastoma.

Published article The complete, open-access, peer-review article on this study can be found at: Davide Chicco, et al., European Journal of Cancer, 193(113291), pages 1-11, 2023. https://doi.org/10.1016/j.ejca.2023.113291