Zbyněk Tüdös1, Petr Kučera1, Paulína Szász1, Igor Hartmann2, Kateřina Langová3, Jozef Škarda4, Filip Čtvrtlík5. 1. Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, I. P. Pavlova 6, 77900, Olomouc, Czech Republic. 2. Department of Urology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, I. P. Pavlova 6, 77900, Olomouc, Czech Republic. 3. Department of Medical Biophysics, Faculty of Medicine and Dentistry, Palacky University, Hněvotínská 3, Olomouc, 775 15, Czech Republic. 4. Department of Clinical and Molecular Pathology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Hněvotínská 3, 775 15, Olomouc, Czech Republic. 5. Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, I. P. Pavlova 6, 77900, Olomouc, Czech Republic. filip.ctvrtlik@fnol.cz.
Abstract
PURPOSE: The aim was to determine the optimal slice thickness of CT images and the optimal threshold of negative voxels for CT histogram analysis to distinguish adrenal adenomas from non-adenomas with a mean attenuation more than 10 Hounsfield units (HU). METHODS: Volume CT histogram analysis of 83 lipid-poor adenomas and 80 non-adenomas was performed retrospectively. The volume of interest was extracted from each adrenal lesion, and the mean attenuation, standard deviation (SD), and percentage of voxels with a negative CT value were recorded using reconstructions with different slice thicknesses (5 mm, 2.5 mm, 1.25 mm). The percentage of negative voxels was correlated with SD as a measure of image noise and with the reference splenic tissue values. The sensitivity, specificity, and positive predictive value (PPV) for the identification of adenomas were calculated using reconstructions with different slice thicknesses and three different thresholds of negative voxels (1%, 5%, 10%). RESULTS: The percentage of negative voxels increased with a thinner slice thickness and correlated with increasing CT image noise in adenomas, non-adenomas, and spleen. Using a threshold of 10% negative voxels and a slice thickness of 5 mm, we reached a sensitivity of 53.0%, specificity of 98.8% and the highest PPV, and thus we propose this combination for clinical use. Other combinations achieved a clearly lower specificity and PPV as a result of the increasing noise in CT images. CONCLUSION: The CT slice thickness significantly affects the result and diagnostic value of histogram analysis. Thin CT slice reconstructions are inappropriate for histogram analysis.
PURPOSE: The aim was to determine the optimal slice thickness of CT images and the optimal threshold of negative voxels for CT histogram analysis to distinguish adrenal adenomas from non-adenomas with a mean attenuation more than 10 Hounsfield units (HU). METHODS: Volume CT histogram analysis of 83 lipid-poor adenomas and 80 non-adenomas was performed retrospectively. The volume of interest was extracted from each adrenal lesion, and the mean attenuation, standard deviation (SD), and percentage of voxels with a negative CT value were recorded using reconstructions with different slice thicknesses (5 mm, 2.5 mm, 1.25 mm). The percentage of negative voxels was correlated with SD as a measure of image noise and with the reference splenic tissue values. The sensitivity, specificity, and positive predictive value (PPV) for the identification of adenomas were calculated using reconstructions with different slice thicknesses and three different thresholds of negative voxels (1%, 5%, 10%). RESULTS: The percentage of negative voxels increased with a thinner slice thickness and correlated with increasing CT image noise in adenomas, non-adenomas, and spleen. Using a threshold of 10% negative voxels and a slice thickness of 5 mm, we reached a sensitivity of 53.0%, specificity of 98.8% and the highest PPV, and thus we propose this combination for clinical use. Other combinations achieved a clearly lower specificity and PPV as a result of the increasing noise in CT images. CONCLUSION: The CT slice thickness significantly affects the result and diagnostic value of histogram analysis. Thin CT slice reconstructions are inappropriate for histogram analysis.