Adrenal adenomas(AA)are the most common benign adrenal lesions, often characterized based on intra-lesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA,particularly LPA, from nonadenoma adrenal lesions (NAL)may be challenging.Texture analysis(TA)can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest.To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach.Study Type: Retrospective, observational study. Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Field Strength/Sequence:Unenhanced T1-weighted in-phase (IP) and out-of-phase (OP) as well as T2-weighted (T2-w)MR images acquired at 3T.Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T2- w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. A total of 138 TA-derived features were extracted;among these,four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP(Mean_Intensity and Maximum_3D_Diameter),and T2-w (Standard_- Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%.The expert radiologist obtained a diagnos- tic accuracy of 73%. McNemar’s test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist.Data Conclusion: Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. Level of Evidence:4 Technical Efficacy:Stage 2

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach

Romeo, Valeria;Maurea, Simone;Cuocolo, Renato
;
Petretta, Mario
Writing – Review & Editing
;
Mainenti, Pier Paolo;Verde, Francesco;COPPOLA, MILENA;Dell'Aversana, Serena;Brunetti, Arturo
2018

Abstract

Adrenal adenomas(AA)are the most common benign adrenal lesions, often characterized based on intra-lesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA,particularly LPA, from nonadenoma adrenal lesions (NAL)may be challenging.Texture analysis(TA)can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest.To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach.Study Type: Retrospective, observational study. Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Field Strength/Sequence:Unenhanced T1-weighted in-phase (IP) and out-of-phase (OP) as well as T2-weighted (T2-w)MR images acquired at 3T.Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T2- w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. A total of 138 TA-derived features were extracted;among these,four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP(Mean_Intensity and Maximum_3D_Diameter),and T2-w (Standard_- Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%.The expert radiologist obtained a diagnos- tic accuracy of 73%. McNemar’s test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist.Data Conclusion: Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. Level of Evidence:4 Technical Efficacy:Stage 2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/698558
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