Keywords: Machine Learning/Artificial Intelligence, MultimodalIn this study, we proposed a promising method to distinguish the double expression lymphoma (DEL) from the non-double expression lymphoma (non-DEL) in primary central nervous system lymphoma (PCNSL) by using multiparametric MRI-based machine learning. The results showed that clinical characteristics and MR imaging features had no significant differences in distinguishing DEL from non-DEL . However, radiomics features could differentiate the two status and the best model in this study was SVMlinear with the combined four sequence group (AUCmean = 0.89±0.04). So multiparametric MRI based machine learning is promising in predicting DEL status in PCNSL.
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