RESUMO
BACKGROUND: Temporal bone skull base pathologies represent a complex differential because they can be radiographically obscure and difficult to diagnose without biopsy. Radiomics involves the use of mathematical quantification of imaging data beyond simple intensity, size, and location to inform diagnosis and prognosis. We examined the feasibility of using radiomic parameters to help predict temporal bone tumor type. METHODS: A total of 117 radiomic parameters were analyzed from 5 magnetic resonance imaging sequences (T1 without contrast, T1 with contrast, T2, fluid-attenuated inversion recovery, apparent diffusion coefficient [ADC]) for each tumor. Statistical analysis was used to delineate known primary, metastatic/secondary, and lymphoma lesions using radiomics. RESULTS: The mean tumor volumes for the 14 primary, 12 secondary, and 8 lymphoma lesions were 2.98 ± 2.11, 3.28 ± 2.31, and 12.16 ± 7.1 cm3, respectively (P = 0.2). No significant differences in mean intensity values for any sequence helped distinguish tumors (P > 0.05), but 6 radiomic parameters were significantly correlated with diagnostic accuracy. Discriminant analysis using a stepwise algorithm generated a model where radiomic parameters for T1 cluster prominence, ADC dependence nonuniformity, T1 with contrast zone percentage, and ADC informational measure of correlation 2 achieved the best predictive model (P = 0.0001). These significant characteristics were often indirect measures of tumor heterogeneity on different magnetic resonance imaging sequences. CONCLUSIONS: These data suggest that quantitative measures of tumor heterogeneity can be discriminatory of pathology and might be integrated into clinical workflow. Although this pilot study requires further validation, these data support the exploration of radiomics in temporal bone radiographic diagnostics.
