Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening.

Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI) are emerging alternatives to standard MRI for supplemental breast cancer screening. Purpose To compare the diagnostic performance of CEM, ABMRI, and standard MRI. Materials and Methods This single-institution, prospective, blinded reader study included female participants referred for breast MRI from January 2018 to June 2021. CEM was performed within 14 days of standard MRI; ABMRI was produced from standard MRI images. Two readers independently interpreted each CEM and ABMRI after a washout period. Examination-level performance metrics calculated were recall rate, cancer detection, and false-positive biopsy recommendation rates per 1000 examinations and sensitivity, specificity, and positive predictive value of biopsy recommendation. Bootstrap and permutation tests were used to calculate 95% CIs and compare modalities. Results Evaluated were 492 paired CEM and ABMRI interpretations from 246 participants (median age, 51 years; IQR, 43-61 years). On 49 MRI scans with lesions recommended for biopsy, nine lesions showed malignant pathology. No differences in ABMRI and standard MRI performance were identified. Compared with standard MRI, CEM demonstrated significantly lower recall rate (14.0% vs 22.8%; difference, -8.7%; 95% CI: -14.0, -3.5), lower false-positive biopsy recommendation rate per 1000 examinations (65.0 vs 162.6; difference, -97.6; 95% CI: -146.3, -50.8), and higher specificity (87.8% vs 80.2%; difference, 7.6%; 95% CI: 2.3, 13.1). Compared with standard MRI, CEM had significantly lower cancer detection rate (22.4 vs 36.6; difference, -14.2; 95% CI: -28.5, -2.0) and sensitivity (61.1% vs 100%; difference, -38.9%; 95% CI: -66.7, -12.5). The performance differences between CEM and ABMRI were similar to those observed between CEM and standard MRI. Conclusion ABMRI had comparable performance to standard MRI and may support more efficient MRI screening. CEM had lower recall and higher specificity compared with standard MRI or ABMRI, offset by lower cancer detection rate and sensitivity compared with standard MRI. These trade-offs warrant further consideration of patient population characteristics before widespread screening with CEM. Clinical trial registration no. NCT03517813 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation.

BACKGROUND: Artificial intelligence (AI) may improve cancer detection and risk prediction during mammography screening, but radiologists' preferences regarding its characteristics and implementation are unknown. PURPOSE: To quantify how different attributes of AI-based cancer detection and risk prediction tools affect radiologists' intentions to use AI during screening mammography interpretation. MATERIALS AND METHODS: Through qualitative interviews with radiologists, we identified five primary attributes for AI-based breast cancer detection and four for breast cancer risk prediction. We developed a discrete choice experiment based on these attributes and invited 150 US-based radiologists to participate. Each respondent made eight choices for each tool between three alternatives: two hypothetical AI-based tools versus screening without AI. We analyzed samplewide preferences using random parameters logit models and identified subgroups with latent class models. RESULTS: Respondents (n = 66; 44% response rate) were from six diverse practice settings across eight states. Radiologists were more interested in AI for cancer detection when sensitivity and specificity were balanced (94% sensitivity with <25% of examinations marked) and AI markup appeared at the end of the hanging protocol after radiologists complete their independent review. For AI-based risk prediction, radiologists preferred AI models using both mammography images and clinical data. Overall, 46% to 60% intended to adopt any of the AI tools presented in the study; 26% to 33% approached AI enthusiastically but were deterred if the features did not align with their preferences. CONCLUSION: Although most radiologists want to use AI-based decision support, short-term uptake may be maximized by implementing tools that meet the preferences of dissuadable users.

Multilevel Factors Associated With Time to Biopsy After Abnormal Screening Mammography Results by Race and Ethnicity.

Importance: Diagnostic delays in breast cancer detection may be associated with later-stage disease and higher anxiety, but data on multilevel factors associated with diagnostic delay are limited. Objective: To evaluate individual-, neighborhood-, and health care-level factors associated with differences in time from abnormal screening to biopsy among racial and ethnic groups. Design, Setting, and Participants: This prospective cohort study used data from women aged 40 to 79 years who had abnormal results in screening mammograms conducted in 109 imaging facilities across 6 US states between 2009 and 2019. Data were analyzed from February 21 to November 4, 2021. Exposures: Individual-level factors included self-reported race and ethnicity, age, family history of breast cancer, breast density, previous breast biopsy, and time since last mammogram; neighborhood-level factors included geocoded education and income based on residential zip codes and rurality; and health care-level factors included mammogram modality, screening facility academic affiliation, and facility onsite biopsy service availability. Data were also assessed by examination year. Main Outcome and Measures: The main outcome was unadjusted and adjusted relative risk (RR) of no biopsy within 30, 60, and 90 days using sequential log-binomial regression models. A secondary outcome was unadjusted and adjusted median time to biopsy using accelerated failure time models. Results: A total of 45 186 women (median [IQR] age at screening, 56 [48-65] years) with 46 185 screening mammograms with abnormal results were included. Of screening mammograms with abnormal results recommended for biopsy, 15 969 (34.6%) were not resolved within 30 days, 7493 (16.2%) were not resolved within 60 days, and 5634 (12.2%) were not resolved within 90 days. Compared with White women, there was increased risk of no biopsy within 30 and 60 days for Asian (30 days: RR, 1.66; 95% CI, 1.31-2.10; 60 days: RR, 1.58; 95% CI, 1.15-2.18), Black (30 days: RR, 1.52; 95% CI, 1.30-1.78; 60 days: 1.39; 95% CI, 1.22-1.60), and Hispanic (30 days: RR, 1.50; 95% CI, 1.24-1.81; 60 days: 1.38; 95% CI, 1.11-1.71) women; however, the unadjusted risk of no biopsy within 90 days only persisted significantly for Black women (RR, 1.28; 95% CI, 1.11-1.47). Sequential adjustment for selected individual-, neighborhood-, and health care-level factors, exclusive of screening facility, did not substantially change the risk of no biopsy within 90 days for Black women (RR, 1.27; 95% CI, 1.12-1.44). After additionally adjusting for screening facility, the increased risk for Black women persisted but showed a modest decrease (RR, 1.20; 95% CI, 1.08-1.34). Conclusions and Relevance: In this cohort study involving a diverse cohort of US women recommended for biopsy after abnormal results on screening mammography, Black women were the most likely to experience delays to diagnostic resolution after adjusting for multilevel factors. These results suggest that adjustment for multilevel factors did not entirely account for differences in time to breast biopsy, but unmeasured factors, such as systemic racism and other health care system factors, may impact timely diagnosis.

Breast Cancer Conspicuity on Computed Versus Acquired High b-Value Diffusion-Weighted MRI.

RATIONALE AND OBJECTIVES: On unenhanced diffusion-weighted imaging (DWI), computing or synthesizing high b-value images from lower b-value acquisitions can enhance breast cancer visibility. This study aimed to evaluate relative lesion conspicuity on computed versus acquired diffusion-weighted images and investigate clinical characteristics influencing optimal b-values. MATERIALS AND METHODS: Women with newly diagnosed breast cancer were prospectively enrolled and underwent 3T breast MRI with DWI. Lesion contrast-to-noise ratio (CNR) was measured across a range of b-values (0-2500 s/mm2) for computed and acquired DWI. Three readers independently compared lesion visibility between computed and acquired DWI and selected the optimal b-value. Computed versus acquired DWI was compared quantitatively based on CNR by paired t-test and qualitatively based on reader preference using a sign test. Optimal b-values by qualitative and quantitative assessment were compared by paired t-test, and associations with clinical characteristics were assessed by Wilcoxon rank sum test. RESULTS: The study included 30 women (median age, 48 years); 28 with invasive carcinoma, 2 DCIS. Lesion CNR was higher on acquired versus computed images (p = 0.018), while lesion visibility by reader assessment was not different (p = 0.36). Optimal b-values selected by readers (mean, b = 1411 ± 383 s/mm2) were slightly higher than those based on peak CNR (b = 1233 ± 463 s/mm2, p = 0.023), and were higher for younger (≤50 years) versus older women (p = 0.002) and dense versus nondense breasts (p = 0.015). CONCLUSION: Lesion CNR on computed high b-value images was slightly reduced versus acquired images, but our study suggests that this did not significantly impact lesion visibility. Computing high b-value images offers extra flexibility to adjust b-value during interpretation.

Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study.

BACKGROUND: Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging. METHODS: With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [λ1], radial diffusivity [(λ2 + λ3)/2], and empirical difference [λ1 - λ3]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment. RESULTS: The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37-0.42 per 1-SD increase, p < 0.001 for each), as was higher FA (OR = 1.45, p = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p < 0.001) and DTI-only (AUC = 0.75, p = 0.002) models. CONCLUSIONS: DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

A Checklist Manifesto: Effectiveness of Checklist Use in Hands-On Simulation Examining Competency in Contrast Reaction Management in a Randomized Controlled Study.

OBJECTIVE: The purpose of this article is to assess the performance of a contrast reaction management checklist for optimal management of a contrast reaction scenario created using a high-fidelity hands-on simulation. MATERIALS AND METHODS: A safety checklist was designed that presented the five adverse events that most commonly occur after administration of IV contrast medium as well as their step-by-step management. Forty-three radiology residents were randomized into two groups, a checklist group (n = 22) and a control group (n = 21), as stratified by postgraduate year. Participants took written tests involving multiple-choice questions 2 months before and immediately after participating in the high-fidelity simulation scenario, which was videotaped and independently evaluated by three graders. RESULTS: Both groups had similar scores on the multiple-choice question tests taken before and after participation in the simulation (p = 0.35 and p = 0.62, respectively). In the simulation, the checklist group scored significantly higher than the control group with regard to their overall management of a severe contrast reaction (85.1% vs 64.8%; p = 0.001), including individual scores for first-line treatment of bronchospasm (97.0% vs 91.3%; p = 0.035) and use of the correct route of administration and dose of epinephrine (77.3% vs 45.2%; p = 0.021). CONCLUSION: A standardized contrast reaction management checklist can reduce the number of treatment errors that occur during a simulated severe contrast reaction, particularly with regard to proper administration of epinephrine and treatment of bronchospasm. Such a checklist could be used by radiologists, technologists, and nurses to improve patient safety as a result of improved contrast reaction management and teamwork skills.

Spontaneous Intracranial Hypotension With Site of Leak Detected Only After 111In-DTPA Cisternogram.

A 54-year-old man with a 3-week history of orthostatic headache and acute on chronic subdural hematoma presented with imaging findings suggestive of spontaneous intracranial hypotension. Three myelograms were negative for leak, and nontargeted epidural blood patches did not result in symptom relief. A cerebrospinal fluid leak study using In-DTPA with SPECT/CT demonstrated a focal area of asymmetric activity at the left C2 nerve root. A left C2 root tie-off, targeted epidural blood patch, and Dura seal glue resulted in resolution of patient symptomatology highlighting the importance of fused SPECT/CT images in detection of an occult cerebral spinal fluid leak.

Diffusion-weighted imaging reflects variable cellularity and stromal density present in breast fibroadenomas.

OBJECTIVE: To determine the underlying histopathologic features influencing apparent diffusion coefficient (ADC) values of breast fibroadenomas. MATERIALS AND METHODS: Biopsy-proven fibroadenomas (n=26) initially identified as suspicious on breast magnetic resonance imaging (MRI) were retrospectively evaluated. Histopathologic assessments of lesion cellularity and stromal type were compared with ADC measures on diffusion-weighted MRI. RESULTS: Presence of epithelial hyperplasia (increased cellularity) and dense collagenous stroma were both significantly associated with lower lesion ADC values (P=.02 and .004, respectively). CONCLUSION: Variations in epithelial cellularity and stromal type influence breast lesion ADC values and may explain the wide range of ADC measures observed in benign fibroadenomas.

Can MRI biomarkers at 3 T identify low-risk ductal carcinoma in situ?

OBJECTIVE: The objective was to explore whether 3-T magnetic resonance imaging (MRI) can identify low-risk ductal carcinoma in situ (DCIS). METHODS: Dynamic contrast-enhanced and diffusion-weighted (DWI) MRI features of 36 DCIS lesions [8 low risk, Van Nuys Pathologic Classification (VNPC) 1; 28 high risk, VNPC 2/3] were reviewed. An MRI model that best identified low-risk DCIS was determined using multivariate logistic regression. RESULTS: Low-risk DCIS exhibited different DWI properties [i.e., higher contrast-to-noise ratio (P=.02) and lower normalized apparent diffusion coefficients (P=.04)] than high-risk DCIS. A model combining these DWI features provided best performance (area under receiver operating characteristic curve =0.86). CONCLUSIONS: DWI may help identify DCIS lesions requiring less therapy.

Suspicious axillary lymph nodes identified on clinical breast MRI in patients newly diagnosed with breast cancer: can quantitative features improve discrimination of malignant from benign?

RATIONALE AND OBJECTIVES: To determine whether quantitative dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) features can discriminate malignant from benign axillary lymph nodes (ALNs) identified as suspicious on clinical breast MRI in patients newly diagnosed with breast cancer. MATERIALS AND METHODS: After approval from institutional review board, all clinical breast MR examinations performed from March 2006 through January 2010 describing at least one morphologically suspicious ipsilateral ALN in patients with newly diagnosed breast cancer were identified. Each suspicious ALN underwent ultrasound-guided core needle biopsy, and nodes with benign results were subsequently sampled surgically. Quantitative DCE and DW MRI parameters (diameters, volume, enhancement kinetics, and apparent diffusion coefficients [ADC]) were measured for each suspicious ALN and a representative contralateral normal node, and each feature was compared between the ALN groups (normal, benign, and malignant). RESULTS: Thirty-four suspicious ALNs (18 malignant and 16 benign) and 34 contralateral normal-appearing ALNs were included. Suspicious malignant and benign nodes exhibited larger size, greater volume, and lower ADCs than normal ALNs (P < .05). Among suspicious ALNs, the only quantitative measure that discriminated between malignant from benign outcome was percent of ALN demonstrating washout kinetics (P = .02). CONCLUSIONS: In ALNs deemed morphologically suspicious on breast MRI, quantitative MRI features show little value in identifying those with malignant etiology.

Nonmalignant breast lesions: ADCs of benign and high-risk subtypes assessed as false-positive at dynamic enhanced MR imaging.

PURPOSE: To evaluate the diffusion-weighted (DW) imaging characteristics of nonmalignant lesion subtypes assessed as false-positive findings at conventional breast magnetic resonance (MR) imaging. MATERIALS AND METHODS: This HIPAA-compliant retrospective study had institutional review board approval, and the need for informed patient consent was waived. Lesions assessed as Breast Imaging Reporting and Data System category 4 or 5 at clinical dynamic contrast material-enhanced MR imaging that subsequently proved nonmalignant at biopsy were retrospectively reviewed. One hundred seventy-five nonmalignant breast lesions in 165 women were evaluated. Apparent diffusion coefficients (ADCs) from DW imaging (b = 0, 600 sec/mm(2)) were calculated for each lesion and were compared between subtypes and with an ADC threshold of 1.81 × 10(-3) mm(2)/sec (determined in a prior study to achieve 100% sensitivity). RESULTS: Eighty-one (46%) lesions exhibited ADCs greater than the predetermined threshold. The most prevalent lesion subtypes with mean ADCs above the threshold were fibroadenoma ([1.94 ± 0.38 {standard deviation}] × 10(-3) mm(2)/sec; n = 30), focal fibrosis ([1.84 ± 0.48] × 10(-3) mm(2)/sec; n = 19), normal tissue ([1.81 ± 0.47] × 10(-3) mm(2)/sec; n = 13), apocrine metaplasia ([2.01 ± 0.38] × 10(-3) mm(2)/sec; n = 13), usual ductal hyperplasia ([1.83 ± 0.49] × 10(-3) mm(2)/sec; n = 12), and inflammation ([1.95 ± 0.46] × 10(-3) mm(2)/sec; n = 10). Atypical ductal hyperplasia ([1.48 ± 0.36] × 10(-3) mm(2)/sec; n = 23) was the most common lesion subtype with ADC below the threshold. Lymph nodes exhibited the lowest mean ADC of all nonmalignant lesions ([1.28 ± 0.23] × 10(-3) mm(2)/sec; n = 4). High-risk lesions (atypical ductal hyperplasia and lobular neoplasia) showed significantly lower ADCs than other benign lesions (P < .0001) and were the most common lesions with ADCs below the threshold. CONCLUSION: Assessing ADC along with dynamic contrast-enhanced MR imaging features may decrease the number of avoidable false-positive findings at breast MR imaging and reduce the number of preventable biopsies. The ability of DW imaging to help differentiate high-risk lesions requiring additional work-up from other nonmalignant subtypes may further improve patient care. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112672/-/DC1.

Improved B1 homogeneity of 3 Tesla breast MRI using dual-source parallel radiofrequency excitation.

PURPOSE: To compare breast MRI B(1) homogeneity at 3 Tesla (T) with and without dual-source parallel radiofrequency (RF) excitation. MATERIALS AND METHODS: After institutional review board approval, we evaluated 14 consecutive breast MR examinations performed at 3T that included three-dimensional B(1) maps created separately with conventional single-source and dual-source parallel RF excitation techniques. We measured B(1) values (expressed as % of intended B(1) ) on each B(1) map at nipple level in multiple bilateral locations: anterior, lateral, central, medial, and posterior. Mean whole breast and location specific B(1) values were calculated and compared between right and left breasts using paired t-test. RESULTS: Mean whole breast B(1) values differed significantly between right and left breasts with standard single-source RF excitation (difference L-R, Δ = 9.2%; P < 0.001) but not with dual-source parallel RF excitation (Δ = 2.3%; P = 0.085). Location specific B(1) values differed significantly between right and left on single-source in the lateral (P = 0.014), central (P = 0.0001), medial (P = 0.0013), and posterior (P < 0.0001) locations. Conversely, mean B(1) values differed significantly on dual-source parallel RF excitation for only the anterior (P = 0.030) and lateral (P = 0.0003) locations. CONCLUSION: B(1) homogeneity is improved with dual-source parallel RF excitation on 3T breast MRI when compared with standard single-source RF excitation technique.

Benign breast cyst without associated gynecomastia in a male patient: a case report.

Benign simple breast cysts are commonly seen in female breasts and can present as palpable masses. They are distinctly uncommon, however, in the male breast. We report a case of simple benign cyst of the breast in a 58-year-old man newly diagnosed with mantel cell lymphoma. The cyst was first identified incidentally on a staging contrast-enhanced chest computed tomography. Further evaluation with mammography and ultrasound revealed a mass that would be typically characterized as a benign simple cyst, but was biopsied since cysts are not known to occur in male breasts. Pathology results from ultrasound-guided core needle biopsy revealed benign cyst and focal fibrosis which was concordant with the imaging findings. In this case report, we will briefly discuss breast cysts in men and their imaging features including mammography and ultrasound.

Transition from congress abstract to full publication for clinical trials presented at laser meetings.

The present study aims to identify (1) what proportion of abstracts of clinical trials presented at The American Society for Laser Medicine and Surgery (ASLMS) annual meetings are published as full reports, (2) time to publication, and (3) factors that may predict the publication of research in peer-reviewed journals. Two investigators independently hand-searched all abstracts of the ASLMS meetings to identify all reports of clinical trials. Details of sample size, the country of origin, topic of research, type of presentation, type of laser, direction of outcome, and statistical significance were recorded for each abstract. To determine the full publication status of each study, The Cochrane Central Register of Controlled Trials, PubMed, and EMBASE was searched. A total of 198 abstracts were identified. Of these, 87 abstracts (44%) have been fully published. The average time from presentation at the meeting to full publication was 57 months (95% confidence interval = 52-61), and the estimated rate of abstracts published at 1, 2, and 4 years was 15, 30, and 38%, respectively. There is significant tendency for being fully published in high-power laser studies, with USA as country of origin, and orally presented. Our findings supports this opinion that conference abstracts can be an important source for systematic reviews and failure to identify trials presented in congresses might threaten the validity of systematic reviews.