Radiomics and Machine Learning for Prediction of Relapsed and Refractory Primary Central Nervous System Lymphoma

A subset of primary central nervous system lymphoma (PCNSL) may show early relapsed/refractory (R/R) disease after treatments. This study investigated the role of radiomics and machine learning for the prediction of R/R in PCNSL after treatments. Total 46 patients with pathologically confirmed PCNSL were included. Total 321 radiomic features were extracted from various pre-treatment MR sequences in each patient to build prediction models. Among various machine learning algorithms, the best predictive performance with accuracy of 82.6%, precision of 80%, and AUC of 0.85 were obtained in support vector machine.