Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: a comparison of diagnostic performance across different MRI sequences and machine learning techniques

ConclusionRadiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences.Key Points•Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably.•ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model.•Embedded feature selection models perform better than models using a priori feature reduction.
Source: European Radiology - Category: Radiology Source Type: research