Abstract
BACKGROUND: Radiomics is a rapidly growing field in neuro-oncology, but studies have been limited to conventional MRI, and external validation is critically lacking. We evaluated technical feasibility, diagnostic performance, and generalizability of a diffusion radiomics model for identifying atypical primary central nervous system lymphoma (PCNSL) mimicking glioblastoma. METHODS: A total of 1618 radiomics features were extracted from diffusion and conventional MRI from 112 patients (training set, 70 glioblastomas and 42 PCNSLs). Feature selection and classification were optimized using a machine-learning algorithm. The diagnostic performance was tested in 42 patients of internal and external validation sets. The performance was compared with that of human readers (2 neuroimaging experts), cerebral blood volume (90% histogram cutoff, CBV90), and apparent diffusion coefficient (10% histogram, ADC10) using the area under the receiver operating characteristic curve (AUC). RESULTS: The diffusion radiomics was optimized with the combination of recursive feature elimination and a random forest classifier (AUC 0.983, stability 2.52%). In internal validation, the diffusion model (AUC 0.984) showed similar performance with conventional (AUC 0.968) or combined diffusion and conventional radiomics (AUC 0.984) and better than human readers (AUC 0.825-0.908), CBV90 (AUC 0.905), or ADC10 (AUC 0.787) in atypical PCNSL diagnosis. In external validation, the diffusion radiomics showed robustness (AUC 0.944) and performed better than conventional radiomics (AUC 0.819) and similar to combined radiomics (AUC 0.946) or human readers (AUC 0.896-0.930). CONCLUSION: The diffusion radiomics model had good generalizability and yielded a better diagnostic performance than conventional radiomics or single advanced MRI in identifying atypical PCNSL mimicking glioblastoma.