Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients.

PURPOSE: The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients.Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. RESULTS: Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group (P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1-99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9-92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group (P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2-100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3-95.8%). CONCLUSIONS: Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

Accuracy of diffusion-weighted imaging-magnetic resonance in differentiating functional from non-functional pituitary macro-adenoma and classification of tumor consistency.

PURPOSE: The purpose of this study was to determine the accuracy of selected first or second-order histogram features in differentiation of functional types of pituitary macro-adenomas. MATERIALS AND METHODS: Diffusion-weighted imaging magnetic resonance imaging was performed on 32 patients (age mean±standard deviation = 43.09 ± 11.02 years; min = 22 and max = 65 years) with pituitary macro-adenoma (10 with functional and 22 with non-functional tumors). Histograms of apparent diffusion coefficient were generated from regions of interest and selected first or second-order histogram features were extracted. Collagen contents of the surgically resected tumors were examined histochemically using Masson trichromatic staining and graded as containing <1%, 1-3%, and >3% of collagen. RESULTS: Among selected first or second-order histogram features, uniformity ( p = 0.02), 75th percentile ( p = 0.03), and tumor smoothness ( p = 0.02) were significantly different between functional and non-functional tumors. Tumor smoothness > 5.7 × 10-9 (area under the curve = 0.75; 0.56-0.89) had 70% (95% confidence interval = 34.8-93.3%) sensitivity and 33.33% (95% confidence interval = 14.6-57.0%) specificity for diagnosis of functional tumors. Uniformity ≤179.271 had a sensitivity of 60% (95% confidence interval = 26.2-87.8%) and specificity of 90.48% (95% confidence interval = 69.6-98.8%) with area under the curve = 0.76; 0.57-0.89. The 75th percentile >0.7 had a sensitivity of 80% (95% confidence interval = 44.4-97.5%) and specificity of 66.67% (95% confidence interval = 43.0-85.4%) for categorizing tumors to functional and non-functional types (area under the curve = 0.74; 0.55-0.88). Using these cut-offs, smoothness and uniformity are suggested as negative predictive indices (non-functional tumors) whereas 75th percentile is more applicable for diagnosis of functional tumors. CONCLUSION: First or second-order histogram features could be helpful in differentiating functional vs non-functional pituitary macro-adenoma tumors.