Journal of magnetic resonance imaging : JMRI · 2018
Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.
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Romeo Valeria, Maurea Simone, Cuocolo Renato, Petretta Mario, Mainenti Pier Paolo, Verde Francesco, Coppola Milena, Dell'Aversana Serena, Brunetti Arturo
Аннотация
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.
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.
Предметные рубрики MeSH
AdenomaAdolescentAdrenal Gland NeoplasmsAdrenal GlandsAdultAgedAlgorithmsContrast MediaFemaleHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedLipidsMachine LearningMagnetic Resonance ImagingMaleMiddle AgedPattern Recognition, AutomatedReproducibility of ResultsRetrospective StudiesYoung Adult