{\textcopyright} 2018 International Society for Magnetic Resonance in Medicine Background: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional 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. Purpose/Hypothesis: 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. Population/Subjects/Phantom/Specimen/Animal Model: 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. Assessment: 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. Statistical Tests: The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. Results: 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 diagnostic 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. J. Magn. Reson. Imaging 2018.
Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach
Cuocolo, Renato;
2018-01-01
Abstract
{\textcopyright} 2018 International Society for Magnetic Resonance in Medicine Background: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional 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. Purpose/Hypothesis: 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. Population/Subjects/Phantom/Specimen/Animal Model: 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. Assessment: 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. Statistical Tests: The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. Results: 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 diagnostic 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. J. Magn. Reson. Imaging 2018.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
