Clinical differentiation of atypical breast abscesses from necrotic tumour in premenopausal women is challenging and may delay appropriate therapy. In this case report, we present a 36-year-old woman with signs, symptoms and conventional imaging features of malignancy who underwent breast MRI. On diffusion-weighted imaging (DWI), profoundly low apparent diffusion coefficient values were a distinguishing sign of breast abscess from necrotic breast cancer, and helped manage the patient conservatively. We present a companion case of necrotic breast tumour highlighting significant differences in DWI.

Objectives

Contrast-enhanced ultrasound (CEUS) cines of focal liver lesions (FLLs) can be quantitatively analyzed to measure tumor perfusion on a pixel-by-pixel basis for diagnostic indication. However, CEUS cines acquired freehand and during free breathing cause nonuniform in-plane and out-of-plane motion from frame to frame. These motions create fluctuations in the time-intensity curves (TICs), reducing the accuracy of quantitative measurements. Out-of-plane motion cannot be corrected by image registration in 2-dimensional CEUS and degrades the quality of in-plane motion correction (IPMC). A 2-tier IPMC strategy and adaptive out-of-plane motion filter (OPMF) are proposed to provide a stable correction of nonuniform motion to reduce the impact of motion on quantitative analyses.

Materials and methods

A total of 22 cines of FLLs were imaged with dual B-mode and contrast specific imaging to acquire a 3-minute TIC. B-mode images were analyzed for motion, and the motion correction was applied to both B-mode and contrast images. For IPMC, the main reference frame was automatically selected for each cine, and subreference frames were selected in each respiratory cycle and sequentially registered toward the main reference frame. All other frames were sequentially registered toward the local subreference frame. Four OPMFs were developed and tested: subsample normalized correlation (NC), subsample sum of absolute differences, mean frame NC, and histogram. The frames that were most dissimilar to the OPMF reference frame using 1 of the 4 above criteria in each respiratory cycle were adaptively removed by thresholding against the low-pass filter of the similarity curve. Out-of-plane motion filter was quantitatively evaluated by an out-of-plane motion metric (OPMM) that measured normalized variance in the high-pass filtered TIC within the tumor region-of-interest with low OPMM being the goal. Results for IPMC and OPMF were qualitatively evaluated by 2 blinded observers who ranked the motion in the cines before and after various combinations of motion correction steps.

Results

Quantitative measurements showed that 2-tier IPMC and OPMF improved imaging stability. With IPMC, the NC B-mode metric increased from 0.504 ± 0.149 to 0.585 ± 0.145 over all cines (P < 0.001). Two-tier IPMC also produced better fits on the contrast-specific TIC than industry standard IPMC techniques did (P < 0.02). In-plane motion correction and OPMF were shown to improve goodness of fit for pixel-by-pixel analysis (P < 0.001). Out-of-plane motion filter reduced variance in the contrast-specific signal as shown by a median decrease of 49.8% in the OPMM. Two-tier IPMC and OPMF were also shown to qualitatively reduce motion. Observers consistently ranked cines with IPMC higher than the same cine before IPMC (P < 0.001) as well as ranked cines with OPMF higher than when they were uncorrected.

Conclusion

The 2-tier sequential IPMC and adaptive OPMF significantly reduced motion in 3-minute CEUS cines of FLLs, thereby overcoming the challenges of drift and irregular breathing motion in long cines. The 2-tier IPMC strategy provided stable motion correction tolerant of out-of-plane motion throughout the cine by sequentially registering subreference frames that bypassed the motion cycles, thereby overcoming the lack of a nearly stationary reference point in long cines. Out-of-plane motion filter reduced apparent motion by adaptively removing frames imaged off-plane from the automatically selected OPMF reference frame, thereby tolerating nonuniform breathing motion. Selection of the best OPMF by minimizing OPMM effectively reduced motion under a wide variety of motion patterns applicable to clinical CEUS. These semiautomated processes only required user input for region-of-interest selection and can improve the accuracy of quantitative perfusion measurements.

Background

To evaluate the effect of b value or the magnetic field strength (B0) on the sensitivity and specificity of quantitative breast diffusion-weighted imaging (DWI).

Methods

A 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.

Results

The 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).

Conclusions

Better 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.

As the Coronavirus disease 2019 (COVID-19) epidemic begins to stabilize, different medical imaging facilities not directly involved in the COVID-19 epidemic face the dilemma of how to return to regular operation. We hereby discuss various fields of concern in resuming breast imaging services. We examine the concerns for resuming functions of breast imaging services in 2 broad categories, including safety aspects of operating a breast clinic and addressing potential modifications needed in managing common clinical scenarios in the COVID-19 aftermath. Using a stepwise approach in harmony with the relative states of the epidemic, health care system capacity, and the current state of performing breast surgeries (and in compliance with the recommended surgical guidelines) can ensure avoiding pointless procedures and ensure a smooth transition to a fully operational breast imaging facility.

Background

Breast arterial calcification (BAC) on mammograms has emerged as a biomarker of women's cardiovascular disease (CVD) risk, but there is a lack of quantification tools and clinical outcomes studies.

Objectives

This study assessed the association of BAC (both presence and quantity) with CVD outcomes.

Methods

This single-center, retrospective study included women with a screening mammogram from 2007 to 2016. BAC was quantified using an artificial intelligence-generated score, which was assessed as both a binary and continuous variable. Regression analyses evaluated the association between BAC and mortality and a composite of acute myocardial infarction, heart failure, stroke, and mortality. Analyses were adjusted for age, race, diabetes, smoking, blood pressure, cholesterol, and history of CVD and chronic kidney disease.

Results

A total of 18,092 women were included in this study (mean age 56.8 ± 11.0 years; diabetes [13%], hypertension [36%], hyperlipidemia [40%], and smoking [5%]). BAC was present in 4,223 (23%). Over a median follow-up of 6 years, death occurred in 7.8% and 2.3% of women with and without BAC, respectively. The composite occurred in 12.4% and 4.3% of women with and without BAC, respectively. Compared to those without, women with BAC had adjusted HRs of 1.49 (95% CI: 1.33-1.67) for mortality and 1.56 (95% CI: 1.41-1.72) for the composite. Each 10-point increase in the BAC score was associated with higher risk of mortality (HR: 1.08 [95% CI: 1.06-1.11]) and the composite (HR: 1.08 [95% CI: 1.06-1.10]). BAC was especially predictive of future events among younger women.

Conclusions

BAC is independently associated with mortality and CVD, especially among younger women. Measurement of BAC beyond presence adds incremental risk stratification. Quantifying BAC using an artificial intelligence algorithm is feasible, clinically relevant, and may improve personalized CVD risk stratification.

Purpose To assess the performance of computer-aided diagnosis (CAD) systems and to determine the dominant ultrasonographic (US) features when classifying benign versus malignant focal liver lesions (FLLs) by using contrast material-enhanced US cine clips. Materials and Methods One hundred six US data sets in all subjects enrolled by three centers from a multicenter trial that included 54 malignant, 51 benign, and one indeterminate FLL were retrospectively analyzed. The 105 benign or malignant lesions were confirmed at histologic examination, contrast-enhanced computed tomography (CT), dynamic contrast-enhanced magnetic resonance (MR) imaging, and/or 6 or more months of clinical follow-up. Data sets included 3-minute cine clips that were automatically corrected for in-plane motion and automatically filtered out frames acquired off plane. B-mode and contrast-specific features were automatically extracted on a pixel-by-pixel basis and analyzed by using an artificial neural network (ANN) and a support vector machine (SVM). Areas under the receiver operating characteristic curve (AUCs) for CAD were compared with those for one experienced and one inexperienced blinded reader. A third observer graded cine quality to assess its effects on CAD performance. Results CAD, the inexperienced observer, and the experienced observer were able to analyze 95, 100, and 102 cine clips, respectively. The AUCs for the SVM, ANN, and experienced and inexperienced observers were 0.883 (95% confidence interval [CI]: 0.793, 0.940), 0.829 (95% CI: 0.724, 0.901), 0.843 (95% CI: 0.756, 0.903), and 0.702 (95% CI: 0.586, 0.782), respectively; only the difference between SVM and the inexperienced observer was statistically significant. Accuracy improved from 71.3% (67 of 94; 95% CI: 60.6%, 79.8%) to 87.7% (57 of 65; 95% CI: 78.5%, 93.8%) and from 80.9% (76 of 94; 95% CI: 72.3%, 88.3%) to 90.3% (65 of 72; 95% CI: 80.6%, 95.8%) when CAD was in agreement with the inexperienced reader and when it was in agreement with the experienced reader, respectively. B-mode heterogeneity and contrast material washout were the most discriminating features selected by CAD for all iterations. CAD selected time-based time-intensity curve (TIC) features 99.0% (207 of 209) of the time to classify FLLs, versus 1.0% (two of 209) of the time for intensity-based features. None of the 15 video-quality criteria had a statistically significant effect on CAD accuracy-all P values were greater than the Holm-Sidak α-level correction for multiple comparisons. Conclusion CAD systems classified benign and malignant FLLs with an accuracy similar to that of an expert reader. CAD improved the accuracy of both readers. Time-based features of TIC were more discriminating than intensity-based features. ^© RSNA, 2017 Online supplemental material is available for this article.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.