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.
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-adenomatumors.
Authors: S K Yrjänä; H Tuominen; A Karttunen; N Lähdesluoma; E Heikkinen; J Koivukangas Journal: AJNR Am J Neuroradiol Date: 2006 Nov-Dec Impact factor: 3.825
Authors: Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews Journal: Neuroimage Date: 2004 Impact factor: 6.556
Authors: Lorenzo Ugga; Renato Cuocolo; Domenico Solari; Elia Guadagno; Alessandra D'Amico; Teresa Somma; Paolo Cappabianca; Maria Laura Del Basso de Caro; Luigi Maria Cavallo; Arturo Brunetti Journal: Neuroradiology Date: 2019-08-02 Impact factor: 2.804