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Characterization of Sub‐1 cm Breast Lesions Using Radiomics Analysis
Author(s) -
Gibbs Peter,
Onishi Natsuko,
Sadinski Meredith,
Gallagher Katherine M.,
Hughes Mary,
Martinez Danny F.,
Morris Elizabeth A.,
Sutton Elizabeth J.
Publication year - 2019
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26732
Subject(s) - wilcoxon signed rank test , rank correlation , univariate , radiomics , mann–whitney u test , spearman's rank correlation coefficient , nuclear medicine , mathematics , medicine , univariate analysis , statistics , nonparametric statistics , population , correlation , breast mri , pattern recognition (psychology) , multivariate analysis , radiology , artificial intelligence , multivariate statistics , computer science , breast cancer , geometry , environmental health , cancer , mammography
Background Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. Purpose To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. Study Type Retrospective, single center. Population In all, 149 patients, with a total of 165 lesions scored as BI‐RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm 3 . Field Strength/Sequence Higher spatial resolution T 1 ‐weighted dynamic contrast‐enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T. Assessment Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first‐order statistics, gray level co‐occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases). Statistical Tests Comparison of medians was assessed using the nonparametric Mann–Whitney U ‐test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models. Results Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features ( P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75–0.81. High negative (>89%) and positive predictive values (>83%) were found for all models. Data Conclusion Radiomics analysis of small contrast‐enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort.Level of Evidence : 4 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2019;50:1468–1477.