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- W3013504003 abstract "This study aimed to determine whether a combined diagnosis of whole-lesion histogram analysis of T1- and T2-weighted imaging based on support vector machine (SVM) can distinguish pheochromocytoma from adrenal adenoma.A pathology database was retrospectively appraised over a period of 7 years and we obtained 40 histopathologically proven adrenal adenomas and 20 pheochromocytomas with magnetic resonance images. The T1-weighted imaging (T1WI, including both in phase and opposed phase) and T2-weighted imaging (T2WI) images of each patients were analyzed using Mazda software. Nine parameters were selected as indicators of comparison: variance, skewness, kurtosis, mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile. The parameters with differential-diagnosis significance were used to establish the combined diagnostic model of SVM.Among the 9 parameters extracted using histogram analysis, the 1st percentile, 10th percentile, and 50th percentile of T1WI (in phase) and the skewness of T2WI and almost all parameters of T1WI (opposed phase), except variance and 99th percentile, showed statistical significance between groups. Among the above parameters, the area under the curve (AUC) of 10th percentile of T1WI (opposed phase) was the largest with the value of 0.909 (100.0% sensitivity and 80.0% specificity). After the analysis of combined diagnosis was performed, the AUC of SVM model in testing set showed the value of 0.917 (85.0% accuracy).Whole-lesion histogram analysis of T1WI and T2WI may help differentiate adrenal adenomas from pheochromocytomas. Furthermore, the combined diagnosis of T1WI and T2WI histogram based on SVM was more effective than most of individual histogram parameters." @default.
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- W3013504003 date "2020-03-25" @default.
- W3013504003 modified "2023-09-27" @default.
- W3013504003 title "Combined Diagnosis of Whole-Lesion Histogram Analysis of T1- and T2-Weighted Imaging for Differentiating Adrenal Adenoma and Pheochromocytoma: A Support Vector Machine-Based Study" @default.
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- W3013504003 doi "https://doi.org/10.1177/0846537120911736" @default.
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