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

• Published in: Journal of magnetic resonance imaging Vol. 48; no. 1; pp. 198 - 204
• Main Authors: Romeo, Valeria, Maurea, Simone, Cuocolo, Renato, Petretta, Mario, Mainenti, Pier Paolo, Verde, Francesco, Coppola, Milena, Dell'Aversana, Serena, Brunetti, Arturo
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2018
• Subjects: adrenal glands; Artificial intelligence; Classifiers; Diagnostic systems; Feature extraction; Field strength; Image acquisition; Learning algorithms; Lesions; Machine learning; Magnetic resonance imaging; Medical imaging; Neuroendocrine tumors; Object recognition; Parameters; Pattern recognition; Population (statistical); Population studies; Statistical analysis; Statistical tests; Texture; texture analysis; magnetic resonance imaging; machine learning; adrenal glands; texture analysis
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.25954

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.