MR imaging based fractal analysis for differentiating primary CNS lymphoma and glioblastoma.

objectivesThe aim of this study was to differentiate primary central nervous system lymphoma (PCNSL) from glioblastomas (GBM) using the fractal analysis of conventional MRI data. MATERIALS AND

methodsSixty patients with PCNSL and 107 patients with GBM with MRI data available were enrolled. Fractal dimension (FD) and lacunarity values of the tumour region were calculated using fractal analysis. A predictive model combining fractal parameters and anatomical characteristics was built using logistic regression. The role of FD, lacunarity and the predictive model in differential diagnosis was evaluated using receiver-operating characteristic (ROC) curve analysis. The association between fractal parameters and anatomical characteristics of tumours was also investigated.

resultsPCNSL had lower FD values (p < 0.001) and higher lacunarity values (p < 0.001) than GBM. ROC curve analysis revealed that FD, lacunarity, and the predictive model could distinguish PCNSL from GBM (area under the curve: 0.895, 0.776, and 0.969, respectively). The following associations were observed between fractal parameters and anatomical characteristics: multiple lesions were significantly associated with higher lacunarity (p = 0.024), necrosis with higher FD (p = 0.027), corpus callosum involvement with higher lacunarity (p < 0.001) in PCNSL and subventricular zone involvement with higher FD (p < 0.001) in GBM.

conclusionsThe findings of the study indicate that fractal analysis on conventional MRI performs well in distinguishing PCNSL from GBM. KEY POINTS: • Fractal dimension and lacunarity were capable of differentiating PCNSL from GBM. • PCNSL and GBM exhibited different anatomical characteristics. • Fractal parameters were associated with some of these anatomical characteristics.