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 cm3 . FIELD STRENGTH/SEQUENCE: Higher spatial resolution T1 -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.
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 cm3 . FIELD STRENGTH/SEQUENCE: Higher spatial resolution T1 -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.
Authors: Isaac Daimiel Naranjo; Peter Gibbs; Jeffrey S Reiner; Roberto Lo Gullo; Caleb Sooknanan; Sunitha B Thakur; Maxine S Jochelson; Varadan Sevilimedu; Elizabeth A Morris; Pascal A T Baltzer; Thomas H Helbich; Katja Pinker Journal: Diagnostics (Basel) Date: 2021-05-21
Authors: Doris Leithner; Marius E Mayerhoefer; Danny F Martinez; Maxine S Jochelson; Elizabeth A Morris; Sunitha B Thakur; Katja Pinker Journal: J Clin Med Date: 2020-06-14 Impact factor: 4.241
Authors: Natsuko Onishi; Meredith Sadinski; Mary C Hughes; Eun Sook Ko; Peter Gibbs; Katherine M Gallagher; Maggie M Fung; Theodore J Hunt; Danny F Martinez; Amita Shukla-Dave; Elizabeth A Morris; Elizabeth J Sutton Journal: Breast Cancer Res Date: 2020-05-28 Impact factor: 6.466