Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis

Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail.

To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps.

Retrospective, single center.

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 .

Higher spatial resolution T₁ -weighted dynamic contrast-enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T.

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).

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.

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.

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.

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468-1477.