Radiomics Analysis of Contrast-Enhanced Breast MRI for Optimized Modelling of Virtual Prognostic Biomarkers in Breast Cancer

Objective: Breast cancer clinical stage and nodal status are the most clinically significant drivers of patient management, in combination with other pathological biomarkers, such as estrogen receptor (ER), progesterone receptor or human epidermal growth factor receptor 2 (HER2) receptor status and tumor grade. Accurate prediction of such parameters can help avoid unnecessary intervention, including unnecessary surgery. The objective was to investigate the role of magnetic resonance imaging (MRI) radiomics for yielding virtual prognostic biomarkers (ER, HER2 expression, tumor grade, molecular subtype, and T-stage). Materials and Methods: Patients with primary invasive breast cancer who underwent dynamic contrast-enhanced (DCE) breast MRI between July 2013 and July 2016 in a single center were retrospectively reviewed. Age, N-stage, grade, ER and HER2 status, and Ki-67 (%) were recorded. DCE images were segmented and Haralick texture features were extracted. The Bootstrap Lasso feature selection method was used to select a small subset of optimal texture features. Classification of the performance of the final model was assessed with the area under the receiver operating characteristic curve (AUC). Results: Median age of patients (n = 209) was 49 (21–79) years. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the model for differentiating N0 vs N1-N3 was: 71%, 79%, 76%, 74%, 75% [AUC = 0.78 (95% confidence interval (CI) 0.72–0.85)], N0-N1 vs N2–N3 was 81%, 59%, 24%, 95%, 62% [AUC = 0.74 (95% CI 0.63–0.85)], distinguishing HER2(+) from HER2(-) was 79%, 48%, 34%, 87%, 56% [AUC = 0.64 (95% CI 0.54–0.73)], high nuclear grade (grade 2–3) vs low grade (grades 1) was 56%, 88%, 96%, 29%, 61% [AUC = 0.71 (95% CI 0.63–0.80)]; and for ER (+) vs ER(-) status the [AUC=0.67 (95% CI 0.59–0.76)]. Radiomics performance in distinguishing triple-negative vs other molecular subtypes was [0.60 (95% CI 0.49–0.71)], and Luminal A [0.66 (95% CI 0.56–0.76)]. Conclusion: Quantitative radiomics using MRI contrast texture shows promise in identifying aggressive high grade, node positive triple negative breast cancer, and correlated well with higher nuclear grades, higher T-stages, and N-positive stages

Effects of magnetic field strength and b value on the sensitivity and specificity of quantitative breast diffusion-weighted MRI

BackgroundTo evaluate the effect of b value or the magnetic field strength (B0) on the sensitivity and specificity of quantitative breast diffusion-weighted imaging (DWI).MethodsA total of 126 patients underwent clinical breast MRI that included pre-contrast DWI imaging using b values of both 1,000 and 1,500 s/mm2 at either 1.5 T (n=86) or 3.0 T (n=40). Quantitative apparent diffusion coefficients (ADC) were measured and compared for 18 benign, 33 malignant lesions, and 126 normal breast tissues. Optimal ADCmean threshold for differentiating benign and malignant lesions was estimated and the effect of b values and B0 were examined using a generalized estimating equations (GEE) model.ResultsThe optimal ADCmean threshold was 1.235×10-3 mm2/s for b value of 1,000 and 0.934×10-3 mm2/s for b value of 1,500. Using these thresholds, the sensitivities and specificities were 96% and 89% (b value =1,000, B0 =1.5 T), 89% and 98% (b value =1,000, B0 =3.0 T), 88% and 96% (b value =1,500, B0 =1.5 T), and 67% and 100% (b value =1,500, B0 =3.0 T). No significant difference was found between different B0 (P=0.26) or b values (P=0.28).ConclusionsBetter sensitivity is achieved with DWI of b value =1,000 than with b value =1,500. However, b value and B0 do not significantly impact diagnostic performance of DWI when using appropriate thresholds

COVID-19 and Breast Radiologist Wellness: Impact of Gender, Financial Loss, and Childcare Need.

PurposeThe purpose of this study was to evaluate the emotional and financial impact of coronavirus disease 2019 (COVID-19) on breast radiologists to understand potential consequences on physician wellness and gender disparities in radiology.MethodsA 41-question survey was distributed from June to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Psychological distress and financial loss scores were calculated on the basis of survey responses and compared across gender and age subgroups. A multivariate logistic model was used to identify factors associated with psychological distress scores.ResultsA total of 628 surveys were completed (18% response rate); the mean respondent age was 52 ± 10 years, and 79% were women. Anxiety was reported by 68% of respondents, followed by sadness (41%), sleep problems (36%), anger (25%), and depression (23%). A higher psychological distress score correlated with female gender (odds ratio [OR], 1.9; P = .001), younger age (OR, 0.8 per SD; P = .005), and a higher financial loss score (OR, 1.4; P < .0001). Participants whose practices had not initiated wellness efforts specific to COVID-19 (54%) had higher psychological distress scores (OR, 1.4; P = .03). Of those with children at home, 38% reported increased childcare needs, higher in women than men (40% versus 29%, P < .001). Thirty-seven percent reported that childcare needs had adversely affected their jobs, which correlated with higher psychological distress scores (OR, 2.2-3.3; P < .05).ConclusionsPsychological distress was highest among younger and female respondents and those with greater pandemic-specific childcare needs and financial loss. Practice-initiated COVID-19-specific wellness efforts were associated with decreased psychological distress. Policies are needed to mitigate pandemic-specific burnout and worsening gender disparities