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To determine the imaging changes and their associated positive predictive value (PPV) for invasive breast cancer in women undergoing active monitoring for ductal carcinoma in situ (DCIS). In this seven-year follow-up retrospective IRB-exempted cohort study, we reviewed patients diagnosed with DCIS who elected active monitoring between 2003 and 2022 at a single academic institution. Imaging characteristics, histopathology at initial diagnosis, and subsequent follow-up were recorded. Low-risk DCIS was defined as low or intermediate grade and hormone receptor (HR) positive (estrogen and/or progesterone receptor positive) disease diagnosed in women at least 40 years of age. Progression was defined as subsequent ipsilateral invasive breast cancer diagnosis. There were 39 patients with a median age of 58.4 years (IQR: 51.1–69.6 years) and a median follow-up of 4.3 years (range: 0.6–16.4 years). Nearly two thirds of patients (64%, 25/39) had stable imaging (range: 0.6–16.4 years) and remained progression-free during active monitoring. Among the remaining 14 patients (36%), there were 24 imaging findings which prompted 22 subsequent core needle biopsies (range: 1–3 biopsies per patient) and two surgical biopsies. The PPV of invasive cancer was 29% (7/24) overall and 38% (3/8) for masses, 33% (3/9) for calcifications, 17% (1/6) for non-mass enhancement, and 0% (0/1) for architectural distortion. Of the radiographic changes prompting an additional biopsy, development of a new mass (38%) and new calcifications (33%) had the highest PPV for invasive progression. Close imaging follow-up should be a critical component for patients undergoing monitoring for DCIS. [ABSTRACT FROM AUTHOR]

Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast cancer detection. This study was to identify factors associated with abnormality scores assigned by the AI software. A retrospective database search was conducted to identify consecutive asymptomatic women who underwent breast cancer surgery between April 2016 and December 2019. A commercially available AI software (Lunit INSIGHT, MMG, Ver. 1.1.4.0) was used for preoperative mammography to assign individual abnormality scores to the lesions and score of 10 or higher was considered as positive detection by AI software. Radiologists without knowledge of the AI results retrospectively assessed the mammographic density and classified mammographic findings into positive and negative finding. General linear model (GLM) analysis was used to identify the clinical, pathological, and mammographic findings related to the abnormality scores, obtaining coefficient β values that represent the mean difference per unit or comparison with the reference value. Additionally, the reasons for non-detection by the AI software were investigated. Among the 1001 index cancers (830 invasive cancers and 171 ductal carcinoma in situs) in 1001 patients, 717 (72%) were correctly detected by AI, while the remaining 284 (28%) were not detected. Multivariable GLM analysis showed that abnormal mammography findings (β = 77.0 for mass, β = 73.1 for calcification only, β = 49.4 for architectural distortion, and β = 47.6 for asymmetry compared to negative; all P s < 0.001), invasive tumor size (β = 4.3 per 1 cm, P < 0.001), and human epidermal growth receptor type 2 (HER2) positivity (β = 9.2 compared to hormone receptor positive, HER2 negative , P = 0.004) were associated with higher mean abnormality score. AI failed to detect small asymmetries in extremely dense breasts, subcentimeter-sized or isodense lesions, and faint amorphous calcifications. Cancers with positive abnormal mammographic findings on retrospective review, large invasive size, HER2 positivity had high AI abnormality scores. Understanding the patterns of AI software performance is crucial for effectively integrating AI into clinical practice. [ABSTRACT FROM AUTHOR]

Metaplastic breast carcinoma (MBC) is an infrequent malignancy with an unfavorable prognosis, and there is a paucity of research on the multimodal imaging features of MBC. This study aimed to provide a comprehensive analysis of the multimodal imaging features, clinicopathological characteristics, and prognosis of MBC. A total of 36 patients with histologically confirmed MBC from 2012 to 2021 were included in the study. We analyzed the pre-treatment multimodal imaging features, including mammography, ultrasonography (US), and magnetic resonance imaging (MRI), as well as clinicopathology and prognosis of MBC. Follow-up data included local recurrence, distant metastasis, and overall survival (OS) rate. MBC patients had a median age of 51 years at diagnosis. The most common histologic subtype was squamous cell carcinoma, with 86.1% of MBC being histological grade 3 and triple negative. The most common mammographic findings were irregular shape, non-calcification, and high density. The predominant US findings included irregular shape, parallel orientation, posterior acoustic enhancement, and hypoecho. On MRI, most masses exhibited irregular shape, spiculate margin, heterogeneous enhancement, Type II time intensity curve, and diffusion restriction on diffusion weighted images determined by apparent diffusion coefficient. According to breast imaging reporting and data system, mammography suggested malignancy in 50% of cases, US indicated a moderate to high suspicion of malignancy in 77.8% of cases, MRI revealed malignancy in all cases. At a median follow-up time of 48 months (range, 8–122 months) for 35 MBC patients, the local recurrence, distant metastasis, and OS rates were 11.4%, 28.6%, and 67.4%, respectively. The benign features of MBC on mammography and US may cause misinterpretation. However, the inclusion of malignant features observed on MRI can improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]

To examine the role of contrast-enhanced mammography (CEM) in the work-up of palpable breast abnormalities. In this single-center combination prospective-retrospective study, women with palpable breast abnormalities underwent CEM evaluation prospectively, comprising the acquisition of low energy (LE) images and recombined images (RI) which depict enhancement, followed by targeted ultrasound (US). Two independent readers retrospectively reviewed the imaging and assigned BI-RADS assessment based on LE alone, LE plus US, RI with LE plus US (CEM plus US), and RI alone. Pathology results or 1-year follow-up imaging served as the reference standard. 237 women with 262 palpable abnormalities were included (mean age, 51 years). Of the 262 palpable abnormalities, 116/262 (44%) had no imaging correlate and 242/262 (92%) were benign. RI alone had better specificity compared to LE plus US (Reader 1, 94% versus 89% (p = 0.009); Reader 2, 93% versus 88% (p = 0.03)), better positive predictive value (Reader 1, 52% versus 42% (p = 0.04); Reader 2, 53% versus 42% (p = 0.04)), and better accuracy (Reader 1, 93% versus 89% (p = 0.05); Reader 2, 93% versus 90% (p = 0.06)). CEM plus US was not significantly different in performance metrics versus LE plus US. RI had better specificity compared to LE in combination with US. There was no difference in performance between CEM plus US and LE plus US, likely reflecting the weight US carries in radiologist decision-making. However, the results indicate that the absence of enhancement on RI in the setting of palpable lesions may help avoid benign biopsies. [ABSTRACT FROM AUTHOR]

Preoperative accurate identification of benign and malignant breast lesions is vital for patients to achieve individualized treatment. This study aimed to develop and validate a mammography-based radiomic nomogram for predicting malignant risk of breast suspicious microcalcifications (MCs). 496 patients with histologically confirmed breast suspicious MCs were randomly divided into the training set (n = 346) and validation set (n = 150). Radiomics features was extracted from the craniocaudal and mediolateral oblique images. Least absolute shrinkage and selection operator algorithm were used to select radiomics features, then radiomics score (Rad-score) was calculated. Univariate analysis was used to identify malignant MCs-related clinical independent risk factors. Multivariate logistic regression was used to establish a clinical-radiomics model by incorporating Rad-score and clinic factors. A nomogram was developed to visualize the clinical-radiomics model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. The Rad-score was consisted of 29 optimal radiomics features. We developed a nomogram by incorporating Rad-score, menopause status, MCs morphology and distribution, the area under the curve value of the combined model was 0.926(95% confidence interval [CI]: 0.878–0.975) for the validation set. The calibration curves and DCA indicated the combined model had favorable calibration and clinical utility. The combined model could be considered as a potential imaging marker to predict malignant risk of breast suspicious MCs. [ABSTRACT FROM AUTHOR]

The new version of the Contrast Enhanced Mammography (CEM) Breast imaging Reporting and Data System (BIRADs) encourages investigations of a new enhancement descriptor: "Lesion Conspicuity" (LC). The study aims to assess the diagnostic performance and the relationship with the receptor profile of this new enhancement descriptor. Three hundred twenty-five patients with 381 breast lesions who underwent CEM before histological assessmentwere selected. Four radiologists, blinded to each other, categorized LC into the following levels: absent, low, moderate, and high. Considering moderate and high evaluations as predictive of malignancy, the diagnostic performance of CEM was calculated using histological results of the biopsy as the gold standard. The association between LC values and the receptor profile of the neoplasms was also evaluated. The median age at the CEM examination was 50 years (IQR: 45-59). Considering the value of LC of the most experienced radiologist with the interpretation of Low Energy images (LE), we obtained a sensitivity (SE) of 91.9% (95% CI: 88.6%-95.2%) and a specificity (SP) of 67.2% (95% CI: 58.9%-75.5%). An association between "high" lesion conspicuity with ER/PgR not expressed (p = 0.025), with Ki-67>20% (p = 0.033), and with Grading G3 (p = 0.020) was observed. The new feature of enhancement, "Lesion Conspicuity", demonstrated satisfactory performance in predicting the malignancy of lesions and significant correlation with the receptor profile of malignant breast neoplasms. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the usual postoperative imaging findings among patients who underwent oncoplastic breast-conserving surgery (BCS) with acellular dermal matrix (ADM) and to assess the clinical utility of mammography (MG) and ultrasonography (US) in follow-ups. This retrospective review of our prospectively collected database of patients who underwent oncoplastic BCS with ADM was conducted with IRB approval and in HIPPA compliance. A total of 105 patients whose follow-up MG and US studies over 2 years after surgery were available were included in this study. Postoperative imaging findings were analyzed based on sequential MG and US follow-ups. The clinical outcomes and pathological data were also reviewed. The diagnostic performance of MG and US for postoperative surveillance was assessed. On MG, ADM presented as an oval (99/105, 94.3%), circumscribed/obscured (65/105, 61.9%), and high-density (56/105, 53.3%) mass with calcifications (54/105, 51.4%). On US, the ADM-filled cavity presented as an oval (93/105, 88.6%), circumscribed (70/105, 66.7%), heterogenous echoic (65/105, 61.9%) mass with marginal/posterior acoustic shadowing (95/105, 90.5%) on US. During the follow-up period (mean, 46.1 months; range, 26-58 months), four tumor recurrences were diagnosed (4/105, 3.8%). One tumor recurrence detected on MG presented as microcalcifications (1/4, 25%), which were differentiated from benign postoperative calcifications due to their suspicious morphology and location. Two additional recurrences (2/4, 50%), which were MG-negative small masses or intraductal lesions, were detected on US. One tumor recurrence was missed on both MG+US and was detected by magnetic resonance imaging performed at clinician's request. The sensitivity and specificity of MG, US, and MG+US for postoperative surveillance were 25%, 50% and 75% and 100%, 98% and 98%, respectively. Recognition and adequate interpretation of the usual findings after oncoplastic BCS with ADM insertion are important in postoperative imaging surveillance. The sole use of MG is not sufficient for the detection of mass-type tumor recurrence due to the density of inserted ADM fillers. Therefore, the combined use of MG+US could serve as a useful postoperative surveillance tool with increased sensitivity in this population. [ABSTRACT FROM AUTHOR]

This study assessed the role of second-look automated breast ultrasound (ABUS) adjunct to mammography (MAM) versus MAM alone in asymptomatic women and compared it with supplementing handheld ultrasound (HHUS). Women aged 45 to 64 underwent HHUS, ABUS, and MAM among six hospitals in China from 2018 to 2022. We compared the screening performance of three strategies (MAM alone, MAM plus HHUS, and MAM plus ABUS) stratified by age groups and breast density. McNemar's test was used to assess differences in the cancer detection rate (CDR), the false-positive biopsy rate, sensitivity, and specificity of different strategies. Of 19,171 women analyzed (mean [SD] age, 51.54 [4.61] years), 72 cases of breast cancer (3.76 per 1000) were detected. The detection rates for both HHUS and ABUS combined with MAM were statistically higher than those for MAM alone (all p < 0.001). There was no significant difference in cancer yields between the two integration strategies. The increase in CRD of the integrated strategies was higher in women aged 45-54 years with denser breasts compared with MAM alone (all p < 0.0167). In addition, the false-positive biopsy rate of MAM plus ABUS was lower than that of MAM plus HHUS (p = 0.025). Moreover, the retraction in ABUS was more frequent in cases detected among MAM-negative results. Integrated ABUS or HHUS into MAM provided similar CDRs that were significantly higher than those for MAM alone in younger women (45-54 years) with denser breasts. ABUS has the potential to avoid unnecessary biopsies and provides specific image features to distinguish malignant tumors from HHUS. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To explore the differential diagnosis of benign and malignant papillary neoplasms on MRI with non-mass enhancement. A total of 48 patients with surgically confirmed papillary neoplasms showing non-mass enhancement were included. Clinical findings, mammography and MRI features were retrospectively analyzed, and lesions were described according to the breast imaging report and data system (BI-RADS). Multivariate analysis of variance was used to compare the clinical and imaging features of benign and malignant lesions. Fifty-three papillary neoplasms were shown on MR images with non-mass enhancement, including 33 intraductal papilloma and 20 papillary carcinomas (9 intraductal papillary carcinoma, 6 solid papillary carcinomas, and 5 invasive papillary carcinoma). Mammography showed amorphous calcification in 20% (6/30), of which 4 were in papilloma and 2 were in papillary carcinoma. On MRI, papilloma mostly showed linear distribution in 54.55% (18/33), clumped enhancement in 36.36% (12/33). Papillary carcinoma showed segmental distribution in 50% (10/20), clustered ring enhancement in 75% (15/20). ANOVA showed age (p = 0.025), clinical symptoms (p < 0.001), apparent diffusion coefficient (ADC) value (p = 0.026), distribution pattern (p = 0.029) and internal enhancement pattern (p < 0.001) were statistically significant between benign and malignant of papillary neoplasms. Multivariate analysis of variance suggested that the internal enhancement pattern was the only statistically significant factor (p = 0.010). Papillary carcinoma on MRI with non-mass enhancement mostly showed internal clustered ring enhancement, while papilloma mostly showed internal clumped enhancement; additional mammography is of limited diagnostic value, and suspected calcification occurs mostly in papilloma. [ABSTRACT FROM AUTHOR]

Mammographic screening detects most breast cancers but there are still women diagnosed with breast cancer between annual mammograms. We aim to identify features that differentiate screen detected breast cancers from interval breast cancer. All screening mammograms (n = 211,517) performed 7/1/2013-6/30/2020 at our institution were reviewed. Patients with breast cancer diagnosed within one year of screening were included and divided into two distinct groups: screen detected cancer group and interval cancer group. Characteristics in these groups were compared using the chi square test, fisher test, and student's T test. A total of 1,232 patients were included (mean age 64 +/- 11). Sensitivity of screening mammography was 92% (1,136 screen detected cancers, 96 interval cancers). Patient age, race, and personal history of breast cancer were similar between the groups (p > 0.05). Patients with interval cancers more often had dense breast tissue (75/96 = 78% versus 694/1136 = 61%, p < 0.001). Compared to screen detected cancers, interval cancers were more often primary tumor stage two or higher (41/96 = 43% versus 139/1136 = 12%, p < 0.001) and regional lymph node stage one or higher (21/96 = 22% versus 132/1136 = 12%, p = 0.003). Interval cancers were more often triple negative (16/77 = 21% versus [48/813 = 6%], p < 0.001) with high Ki67 proliferation indices (28/45 = 62% versus 188/492 = 38%, p = 0.002). Mammographic screening had high sensitivity for breast cancer detection (92%). Interval cancers were associated with dense breast tissue and had higher stage with less favorable molecular features compared to screen detected cancers. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Adenoid cystic carcinoma (ACC) of the breast is a rare type of breast cancer with favorable prognosis. There is limited data on the radiological findings of this rare tumor in literature. The aim of this study is to determine the most common imaging features and review the literature.Materials and Methods: Pathological databases of seven institutions from 2009 to 2021 were retrospectively reviewed, and patients with a diagnosis of ACC of the breast were determined. Thirteen patients whose imaging studies could be recalled from the picture archiving systems (PACS) were included in the study. Clinical and pathological findings as well as follow-up data were recorded. Radiological findings were analyzed and categorized based on BI-RADS 5th edition.Results: There were 16 mass lesions in 13 patients (two multifocal cases, one case with recurrence). Mammography demonstrated 14 masses, while ultrasound (US) demonstrated all. MRI was available in only seven cases, with eight masses. The most common findings were round or oval shape on all modalities (78.57%-93.75%). Other frequent findings were parallel orientation (81.25%), isoechoic or hyperechoic echogenicity (62.5%), high T2 signal (87.5%), restricted diffusion (71.43%), and homogeneous enhancement (62.5%). Mammography, US and MRI showed circumscribed margins resembling a benign lesion in 35.71%, 37.5% and 50% of the lesions respectively. Three patients had a cyst-like echogenicty on US. Half of the lesions were avascular on Doppler US (6/12) and half were soft (2/4) on strain elastography. Although there were benign features on all imaging modalities seperately, all lesions could be categorized as BI-RADS 4 or 5 when the findings were combined. However 9/16 masses were BI-RADS 4A, emphasizing the subtlety of the malignant features.Conclusion: ACC of the breast can present with findings resembling a benign lesion on different imaging modalities. Although combination of all imaging findings correctly indicated the suspicious nature of the lesions in all cases, final classification was BI-RADS 4A in most of them. Radiologists should be aware of the more frequent findings of ACC of the breast for early diagnosis. US findings of isoechoic or hyperechoic appearance, and cyst-like echogenicity have not been reported previously in literature. [ABSTRACT FROM AUTHOR]

While the American College of Radiology recommends annual screening mammography starting at age 40 years, the US Preventive Services Task Force (USPSTF) recommends that screening mammography in women younger than age 50 years should involve shared- decision making (SDM) between clinicians and patients, considering benefits and potential harms in younger women. Using a nationally representative cross-sectional survey, we aimed to evaluate patient-reported reasons and predictors of screening mammography utilization in this age group. Respondents aged 40-49 years from the 2018 National Health Interview Survey (NHIS) without a history of breast cancer were included (response rate 64%). Participants reported sociodemographic variables and reasons they did not engage in mammography screening within the last two years. Multiple variable logistic regression analyses were performed to evaluate the association between sociodemographic characteristics and patient-reported screening mammography use, accounting for complex survey sampling design elements. 1,948 women between the ages of 40-49 years were included. Of this group, (758/1948) 46.6% reported receiving a screening mammogram within the last year, and 1196/1948 (61.4%) reported receiving a screening mammogram within the last two years. The most common reasons for not undergoing screening included: "No reason/never thought about it" 744/1948 (38.2%), "Put it off" 343/1948 (17.6%), "Didn't need it" 331/1948 (16.9%), "Doctor didn't order it" 162/1948 (8.3%), and "I'm too young" 63/1948 (5.3%). Multiple variable analyses demonstrated that lack of health insurance was the strongest predictor of mammography non-engagement (p < 0.001). Deficits in shared- decision-making in women younger than 50 years related to mammography utilization exist. Radiologists may be key in addressing this issue among ambulatory care providers and patients, educating about the benefits and harms of screening younger women, particularly in racial/ethnic minorities and uninsured patients, who experience additional barriers to care and SDM discussions. [ABSTRACT FROM AUTHOR]

Determine whether there are patterns of lesion recall among breast imaging subspecialists interpreting screening mammography, and if so, whether recall patterns correlate to morphologies of screen-detected cancers. This Institutional Review Board-approved, retrospective review included all screening examinations January 3, 2012–October 1, 2018 interpreted by fifteen breast imaging subspecialists at a large academic medical center and two outpatient imaging centers. Natural language processing identified radiologist recalls by lesion type (mass, calcifications, asymmetry, architectural distortion); proportions of callbacks by lesion types were calculated per radiologist. Hierarchical cluster analysis grouped radiologists based on recall patterns. Groups were compared to overall practice and each other by proportions of lesion types recalled, and overall and lesion-specific positive predictive value-1 (PPV1). Among 161,859 screening mammograms with 13,086 (8.1%) recalls, Hierarchical cluster analysis grouped 15 radiologists into five groups. There was substantial variation in proportions of lesions recalled: calcifications 13%–18% (Chi-square 45.69, p < 0.00001); mass 16%–44% (Chi-square 498.42, p < 0.00001); asymmetry 13%–47% (Chi-square 660.93, p < 0.00001) architectural distortion 6%–20% (Chi-square 283.81, p < 0.00001). Radiologist groups differed significantly in overall PPV1 (range 5.6%–8.8%; Chi-square 17.065, p = 0.0019). PPV1 by lesion type varied among groups: calcifications 9.2%–15.4% (Chi-square 2.56, p = 0.6339); mass 5.6%–8.5% (Chi-square 1.31, p = 0.8597); asymmetry 3.4%–5.9% (Chi-square 2.225, p = 0.6945); architectural distortion 5.6%–10.8% (Chi-square 5.810, p = 0.2138). Proportions of recalled lesions did not consistently correlate to proportions of screen-detected cancer. Breast imaging subspecialists have patterns for screening mammography recalls, suggesting differential weighting of imaging findings for perceived malignant potential. Radiologist recall patterns are not always predictive of screen-detected cancers nor lesion-specific PPV1s. [ABSTRACT FROM AUTHOR]

RATIONALE AND OBJECTIVES: We propose a novel convolutional neural network derived pixel-wise breast cancer risk model using mammographic dataset. MATERIALS AND METHODS: An institutional review board approved retrospective case-control study of 1474 mammographic images was performed in average risk women. First, 210 patients with new incidence of breast cancer were identified. Mammograms from these patients prior to developing breast cancer were identified and made up the case group [420 bilateral craniocaudal mammograms]. The control group consisted of 527 patients without breast cancer from the same time period. Prior mammograms from these patients made up the control group [1054 bilateral craniocaudal mammograms]. A convolutional neural network (CNN) architecture was designed for pixel-wise breast cancer risk prediction. Briefly, each mammogram was normalized as a map of z-scores and resized to an input image size of 256 × 256. Then a contracting and expanding fully convolutional CNN architecture was composed entirely of 3 × 3 convolutions, a total of four strided convolutions instead of pooling layers, and symmetric residual connections. L2 regularization and augmentation methods were implemented to prevent overfitting. Cases were separated into training (80%) and test sets (20%). A 5-fold cross validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. RESULTS: The average age of patients between the case and the control groups was not statistically different [case: 57.4years (SD, 10.4) and control: 58.2years (SD, 10.9), p = 0.33]. Breast Density (BD) was significantly higher in the case group [2.39 (SD, 0.7)] than the control group [1.98 (SD, 0.75), p < 0.0001]. On multivariate logistic regression analysis, both CNN pixel-wise mammographic risk model and BD were significant independent predictors of breast cancer risk (p < 0.0001). The CNN risk model showed greater predictive potential [OR = 4.42 (95% CI, 3.4-5.7] compared to BD [OR = 1.67 (95% CI, 1.4-1.9). The CNN risk model achieved an overall accuracy of 72% (95%CI, 69.8-74.4) in predicting patients in the case group. CONCLUSION: Novel pixel-wise mammographic breast evaluation using a CNN architecture can stratify breast cancer risk, independent of the BD. Larger dataset will likely improve our model.

Rationale and Objectives: The Kaiser score (KS) is a simple and intuitive machine-learning derived decision rule for characterizing breast lesions in a clinical setting and screening for breast cancer. The present study aims to investigate the applicability of the KS for contrast-enhanced mammography (CEM) in breast masses, and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). CEM may provide an alternative option for patients with breast masses, especially for those with MRI contraindications., Materials and Methods: Two hundred and seventy-five patients with breast enhanced masses were included in the study from May 2019 to September 2022. Patients were further divided into benign and malignant groups based on pathological diagnosis. The CEM and MRI imaging characteristics of these two groups were analyzed statistically. The paired chi-square and Cohen's kappa coefficient (κ) analysis were used to compare imaging characteristics between CEM and MRI. The Breast Imaging Reporting and Data System (BI-RADS) and KS for CEM and MRI were evaluated based on imaging characteristics. The diagnostic performance of BI-RADS and KS for CEM and MRI was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test., Results: The imaging characteristics of root sign, time-signal intensity curve (TIC/mTIC), margin, internal enhancement pattern (IEP), edema, apparent diffusion coefficient (ADC) values, and suspicious malignant microcalcifications showed significant differences between benign and malignant lesions (all p ≤ 0.011). The detection rate of root sign and margin showed substantial agreement between CEM and MRI (κ = 0.656, κ = 0.640), but IEP, TIC/mTIC, and edema showed poor agreement (κ = 0.380, κ = 0.320, κ = 0.324). For all lesion analyses, the area under the curves (AUCs) of the KS (0.897 ∼ 0.932) were higher than that of BI-RADS (0.691) in CEM (all p < 0.001). The AUC of KS (calcification)-CEM (0.932) was higher than those of both KS-CEM and KS (edema)-CEM (0.897 and 0.899) (all p < 0.001). For subgroup analyses, the AUCs of the KS (0.875 ∼ 0.876) were higher than that of BI-RADS (0.740) in MRI (all p < 0.001). The AUCs of KS-MRI (0.876) and KS (ADC)-MRI (0.875) were similar to those of KS-CEM (0.878) and KS (edema)-CEM (0.870) (all p > 0.100). The AUC of KS (calcification)-CEM (0.934) was slightly higher than those of both KS-MRI (0.876) and KS (ADC)-MRI (0.875), but no significant difference was observed (p = 0.051; p = 0.071)., Conclusion: The KS for CEM provided high diagnostic accuracy in distinguishing breast masses, comparable to that of MRI. The application of KS (calcification)-CEM combined with suspicious malignant microcalcifications can improve diagnostic efficiency with an AUC of 0.932 ∼ 0.934. However, edema did not significantly improve performance when using the KS for CEM., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Integrating learning spacing in medicine has shown promise in enhancing knowledge retention and diagnostic proficiency. While studies demonstrate the effectiveness of spaced learning in various fields, limited research exists on its application in radiological training. This study aims to investigate the impact of intervals in spaced training on radiologists' and trainees' diagnostic performance via mammogram test sets., Methods: 54 radiologists and 101 radiology trainees completed 207 and 458 first-time readings of 9 mammogram test sets between 2019 and 2023. Each test set comprised of 60 mammograms (20 cancer and 40 normal), sourced retrospectively from BreastScreen Australia. Each radiologist evaluated mammograms using the BIRADS lexicon. Readers' performance was compared with truth data and evaluated in terms of specificity, case sensitivity, lesion sensitivity, ROC AUC and JAFROC FOM. The progress of readers' performances in following test sets after the first one was analyzed using Wilcoxon Signed Rank test. The association of participants' performances and the intervals among test sets' completions was investigated using Pearson's test., Results: Significant positive correlations were found between intervals and radiologists' improvement in specificity and JAFROC FOM (P < 0.05). The separation of 4 to 10 days showed the most improvement among radiologists across all metrics, while intervals exceeding 90 days related to highest increase in case sensitivity (5.15%), lesion sensitivity (6.55%), ROC AUC (3.05%) and JAFROC FOM (6.3%). Trainees completing test sets in one day showed positive correlations with their ROC AUC (R=0.45; P = 0.008) and JAFROC FOM (R=0.43; P = 0.02), while those taking a longer time to complete showed negative impacts on case sensitivity (P = 0.009) and ROC AUC (P = 0.02). Remarkable progress in trainees was found in case sensitivity (6.15%), lesion sensitivity (11.6%), ROC AUC (3.5%) and JAFROC FOM (4.35%) with test set intervals of 31-90 days., Conclusions: Radiologists demonstrated superior performance when the training test sets were spaced over longer intervals, whereas trainees exhibited proficiency with shorter time separations. By optimizing the spacing of reviewing and practicing radiological concepts, mammogram readers can bolster memory retention and diagnostic decision-making skills., Competing Interests: Declaration of Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing Interests All authors have no conflicts of interest related to the work described in the study., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival.Materials and Methods: PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria.Results: Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed.Conclusion: The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool. [ABSTRACT FROM AUTHOR]

Aim: To establish a predictive nomogram for malignancy risk stratification of micro-calcifications (MCCs) detected on mammography.Materials and Methods: Consecutive mammograms from January 2017 to March 2021 were retrospectively reviewed. Traditional clinical features were recorded and mammographic features were estimated according to the 5th BI-RADS. A nomogram was developed to graphically predict the malignancy risk based on multivariate logistic regression analysis. The discrimination and calibration performance of the prediction model was assessed.Results: There were 123 cases of suspicious MCCs with final pathological results identified with a malignancy rate of 55.2%. The malignancy rates of subgroups divided according to the morphology and distribution of MCCs, age, menopausal status and the maximum diameter of MCCs were significantly different. Multivariate logistic analysis showed that a menopause status of postmenopausal, maximum diameters of MCCs ≥2 cm, the morphology of MCCs as fine pleomorphic or fine linear or branching, and the distribution of MCCs as linear or segmental were predictive of a higher probability of malignancy. A prediction nomogram was developed based on four risk factors, including menopausal status as well as the maximum diameters, distribution and morphology of the MCCs. The AUC of that nomogram was 0.839 (95%CI:0.771-0.903).Conclusion: In mammography, the morphology, distribution and maximum diameter of MCCs, and the menopausal status are independent predictors of malignant suspicious MCCs and are readily available in the clinical setting. The nomogram developed in this study for individualized malignancy risk stratification of suspicious MCCs shows a reliable discrimination performance. [ABSTRACT FROM AUTHOR]

Rationale and objectivesEvidence is inconsistent about whether radiologists' interpretive performance on a screening mammography test set reflects their performance in clinical practice. This study aimed to estimate the correlation between test set and clinical performance and determine if the correlation is influenced by cancer prevalence or lesion difficulty in the test set.Materials and methodsThis institutional review board-approved study randomized 83 radiologists from six Breast Cancer Surveillance Consortium registries to assess one of four test sets of 109 screening mammograms each; 48 radiologists completed a fifth test set of 110 mammograms 2 years later. Test sets differed in number of cancer cases and difficulty of lesion detection. Test set sensitivity and specificity were estimated using woman-level and breast-level recall with cancer status and expert opinion as gold standards. Clinical performance was estimated using women-level recall with cancer status as the gold standard. Spearman rank correlations between test set and clinical performance with 95% confidence intervals (CI) were estimated.ResultsFor test sets with fewer cancers (N = 15) that were more difficult to detect, correlations were weak to moderate for sensitivity (woman level = 0.46, 95% CI = 0.16, 0.69; breast level = 0.35, 95% CI = 0.03, 0.61) and weak for specificity (0.24, 95% CI = 0.01, 0.45) relative to expert recall. Correlations for test sets with more cancers (N = 30) were close to 0 and not statistically significant.ConclusionsCorrelations between screening performance on a test set and performance in clinical practice are not strong. Test set performance more accurately reflects performance in clinical practice if cancer prevalence is low and lesions are challenging to detect.

Rationale and objectivesLow-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density.Materials and methodsForty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared.ResultsThe measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI.ConclusionsThe preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

Rationale and Objectives: The ACR Breast Commission conducted a member survey to evaluate current practices of reporting breast arterial calcification (BAC) on mammography and to determine perceptions about the value of BAC communication and follow-up recommendations among radiologists.Materials and Methods: In September 2020, an 18-item online survey was emailed to radiologist members of the American College of Radiology (ACR). Questions included radiologist demographics, current BAC reporting practices, follow-up recommendations, and perceptions about BAC. Five-point Likert scales were used and multivariate analysis was performed.Results: Of 598 completed survey responses, up to 87% (522/598) of ACR radiologist members include BAC in mammogram reports. However, only 41% (212/522) of respondents report BAC 'always' or 'most of the time'. Radiologist factors significantly associated with BAC reporting include years in practice and fellowship training with those in practice longer more likely to report BAC (OR 1.10, 95% CI, [1.01-1.20], p = 0.023) and those with fellowship training less likely to report BAC (OR 0.63, 95% CI, [0.42-0.94], p = 0.024). When BAC is reported, 69% (360/522) simply indicate the presence of BAC, 23% (121/522) provide a subjective grading of BAC burden, and 1% (6/522) calculate a BAC score. Among the radiologists reporting BAC, 58% (301/522) make no subsequent recommendations, while the remainder recommend primary care follow-up (39%; 204/522), cardiology evaluation (13%; 68/522), and/or coronary calcium scoring CT (11%; 59/522). Overall, there was agreement from 66% (392/598) of respondents that BAC is a cardiovascular risk factor. However, there was no consensus on whether patients and/or providers should be informed about BAC or whether reporting of BAC should become a standardized practice in breast imaging. Older and more experienced radiologists are more likely to agree that BAC is a cardiovascular risk factor (p = 0.022), providers should be informed about BAC (p = 0.002 and 0.006), BAC reporting should be a standardized practice (p = 0.004 and 0.001), and feel more comfortable informing patients about BAC (p = 0.001 and 0.003).Conclusion: Radiologists' reporting practices and perceptions regarding BAC are not homogeneous. Although many radiologists report BAC to varying degrees, it is not routinely reported or recommended for follow-up in mammogram reports. Experienced radiologists are more likely to include and value BAC in their breast imaging practice. [ABSTRACT FROM AUTHOR]

Rationale and objectivesThe purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation, and spectral material decomposition.Materials and methodsSpectral mammography images from a total of 92 consecutive asymptomatic women (aged 50-69 years) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition.ResultsIn comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07, and 1.00, respectively.ConclusionsThe results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts.

During the COVID-19 pandemic, the disproportionate morbidity and mortality borne by racial minorities, patients of lower socioeconomic status, and patients lacking health insurance reflect the critical role of social determinants of health, which are manifestations of entrenched structural inequities. In radiology, social determinants of health lead to disparate use of imaging services through multiple intersecting contributors, on both the provider and patient side, affecting diagnosis and treatment. Disparities on the provider side include ordering of initial or follow-up imaging studies and providing standard-of-care interventional procedures, while patient factors include differences in awareness of screening exams and confidence in the healthcare system. Disparate utilization of mammography and lung cancer screening lead to delayed diagnosis, while differential provision of minimally invasive interventional procedures contributes to differential outcomes related to treatment. Interventions designed to mitigate social determinants of health could help to equalize the healthcare system. Here we review disparities in access and health outcomes in radiology. We investigate underlying contributing factors in order to identify potential policy changes that could promote more equitable health in radiology. [ABSTRACT FROM AUTHOR]

Rationale and objectivesAs breast magnetic resonance imaging (MRI) use grows, benchmark performance parameters are needed for auditing and quality assurance purposes. We describe the variation in breast MRI abnormal interpretation rates (AIRs) by clinical indication among a large sample of US community practices.Materials and methodsWe analyzed data from 41 facilities across five Breast Cancer Surveillance Consortium imaging registries. Each registry obtained institutional review board approval for this Health Insurance Portability and Accountability Act compliant analysis. We included 11,654 breast MRI examinations conducted in 2005-2010 among women aged 18-79 years. We categorized clinical indications as 1) screening, 2) extent of disease, 3) diagnostic (eg, breast symptoms), and 4) other (eg, short-interval follow-up). We characterized assessments as positive (ie, Breast Imaging Reporting and Data System [BI-RADS] 0, 4, and 5) or negative (ie, BI-RADS 1, 2, and 6) and provide results with BI-RADS 3 categorized as positive and negative. We tested for differences in AIRs across clinical indications both unadjusted and adjusted for patient characteristics and registry and assessed for changes in AIRs by year within each clinical indication.ResultsWhen categorizing BI-RADS 3 as positive, AIRs were 21.0% (95% confidence interval [CI], 19.8-22.3) for screening, 31.7% (95% CI, 29.6-33.8) for extent of disease, 29.7% (95% CI, 28.3-31.1) for diagnostic, and 27.4% (95% CI, 25.0-29.8) for other indications (P < .0001). When categorizing BI-RADS 3 as negative, AIRs were 10.5% (95% CI, 9.5-11.4) for screening, 21.8% (95% CI, 19.9-23.6) for extent of disease, 17.7% (95% CI, 16.5-18.8) for diagnostic, and 13.3% (95% CI, 11.6-15.2) for other indications (P < .0001). The significant differences in AIRs by indication persisted even after adjusting for patient characteristics and registry (P < .0001). In addition, for most indications, there were no significant changes in AIRs over time.ConclusionsBreast MRI AIRs differ significantly by clinical indication. Practices should stratify breast MRI examinations by indication for quality assurance and auditing purposes.

Rationale and objectivesOur objective is to determine whether expected utility (EU) and the area under the receiver operator characteristic (AUC) are consistent with one another as endpoints of observer performance studies in mammography. These two measures characterize receiver operator characteristic performance somewhat differently. We compare these two study endpoints at the level of individual reader effects, statistical inference, and components of variance across readers and cases.Materials and methodsWe reanalyze three previously published laboratory observer performance studies that investigate various x-ray breast imaging modalities using EU and AUC. The EU measure is based on recent estimates of relative utility for screening mammography.ResultsThe AUC and EU measures are correlated across readers for individual modalities (r = 0.93) and differences in modalities (r = 0.94 to 0.98). Statistical inference for modality effects based on multi-reader multi-case analysis is very similar, with significant results (P < .05) in exactly the same conditions. Power analyses show mixed results across studies, with a small increase in power on average for EU that corresponds to approximately a 7% reduction in the number of readers. Despite a large number of crossing receiver operator characteristic curves (59% of readers), modality effects only rarely have opposite signs for EU and AUC (6%).ConclusionsWe do not find any evidence of systematic differences between EU and AUC in screening mammography observer studies. Thus, when utility approaches are viable (i.e., an appropriate value of relative utility exists), practical effects such as statistical efficiency may be used to choose study endpoints.

Rationale and Objectives: Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clinically implemented our deep learning model at the academic breast imaging practice where the model was developed with high clinical acceptance. Our objective was to externally validate our deep learning model on radiologist breast density assessments in a community breast imaging practice.Materials and Methods: Our deep learning model was implemented at a dedicated breast imaging practice staffed by both academic and community breast imaging radiologists in October 2018. Deep learning model assessment of mammographic breast density was presented to the radiologist during routine clinical practice at the time of mammogram interpretation. We identified 2174 consecutive screening mammograms after implementation of the deep learning model. Radiologist agreement with the model's assessment was measured and compared across radiologist groups.Results: Both academic and community radiologists had high clinical acceptance of the deep learning model's density prediction, with 94.9% (academic) and 90.7% (community) acceptance for dense versus nondense categories (p < 0.001). The proportion of mammograms assessed as dense by all radiologists decreased from 47.0% before deep learning model implementation to 41.0% after deep learning model implementation (p < 0.001).Conclusion: Our deep learning model had a high clinical acceptance rate among both academic and community radiologists and reduced the proportion of mammograms assessed as dense. This is an important step to validating our deep learning model prior to potential widespread implementation. [ABSTRACT FROM AUTHOR]

PurposeTo describe recruitment, enrollment, and participation in a study of US radiologists invited to participate in a randomized controlled trial of two continuing medical education (CME) interventions designed to improve interpretation of screening mammography.MethodsWe collected recruitment, consent, and intervention-completion information as part of a large study involving radiologists in California, Oregon, Washington, New Mexico, New Hampshire, North Carolina, and Vermont. Consenting radiologists were randomized to receive either a 1-day live, expert-led educational session; to receive a self-paced DVD with similar content; or to a control group (delayed intervention). The impact of the interventions was assessed using a preintervention-postintervention test set design. All activities were institutional review board approved and HIPAA compliant.ResultsOf 403 eligible radiologists, 151 of 403 (37.5%) consented to participate in the trial and 119 of 151 (78.8%) completed the preintervention test set, leaving 119 available for randomization to one of the two intervention groups or to controls. Female radiologists were more likely than male radiologists to consent to and complete the study (P = .03). Consenting radiologists who completed all study activities were more likely to have been interpreting mammography for 10 years or less compared to radiologists who consented and did not complete all study activities or did not consent at all. The live intervention group was more likely to report their intent to change their clinical practice as a result of the intervention compared to those who received the DVD (50% versus 17.6%, P = .02). The majority of participants in both interventions groups felt the interventions were a useful way to receive CME mammography credits.ConclusionsCommunity radiologists found interactive interventions designed to improve interpretative mammography performance acceptable and useful for clinical practice. This suggests CME credits for radiologists should, in part, be for examining practice skills.

Rationale and objectivesTest sets for assessing and improving radiologic image interpretation have been used for decades and typically evaluate performance relative to gold standard interpretations by experts. To assess test sets for screening mammography, a gold standard for whether a woman should be recalled for additional workup is needed, given that interval cancers may be occult on mammography and some findings ultimately determined to be benign require additional imaging to determine if biopsy is warranted. Using experts to set a gold standard assumes little variation occurs in their interpretations, but this has not been explicitly studied in mammography.Materials and methodsUsing digitized films from 314 screening mammography exams (n = 143 cancer cases) performed in the Breast Cancer Surveillance Consortium, we evaluated interpretive agreement among three expert radiologists who independently assessed whether each examination should be recalled, and the lesion location, finding type (mass, calcification, asymmetric density, or architectural distortion), and interpretive difficulty in the recalled images.ResultsAgreement among the three expert pairs for recall/no recall was higher for cancer cases (mean 74.3 ± 6.5) than for noncancers (mean 62.6 ± 7.1). Complete agreement on recall, lesion location, finding type and difficulty ranged from 36.4% to 42.0% for cancer cases and from 43.9% to 65.6% for noncancer cases. Two of three experts agreed on recall and lesion location for 95.1% of cancer cases and 91.8% of noncancer cases, but all three experts agreed on only 55.2% of cancer cases and 42.1% of noncancer cases.ConclusionVariability in expert interpretive is notable. A minimum of three independent experts combined with a consensus should be used for establishing any gold standard interpretation for test sets, especially for noncancer cases.

Rationale and objectivesThe aim of this study was to describe the impact of a tailored Web-based educational program designed to reduce excessive screening mammography recall.Materials and methodsRadiologists enrolled in one of four mammography registries in the United States were invited to take part and were randomly assigned to receive the intervention or to serve as controls. The controls were offered the intervention at the end of the study, and data collection included an assessment of their clinical practice as well. The intervention provided each radiologist with individual audit data for his or her sensitivity, specificity, recall rate, positive predictive value, and cancer detection rate compared to national benchmarks and peer comparisons for the same measures; profiled breast cancer risk in each radiologist's respective patient populations to illustrate how low breast cancer risk is in population-based settings; and evaluated the possible impact of medical malpractice concerns on recall rates. Participants' recall rates from actual practice were evaluated for three time periods: the 9 months before the intervention was delivered to the intervention group (baseline period), the 9 months between the intervention and control groups (T1), and the 9 months after completion of the intervention by the controls (T2). Logistic regression models examining the probability that a mammogram was recalled included indication of intervention versus control and time period (baseline, T1, and T2). Interactions between the groups and time period were also included to determine if the association between time period and the probability of a positive result differed across groups.ResultsThirty-one radiologists who completed the continuing medical education intervention were included in the adjusted model comparing radiologists in the intervention group (n = 22) to radiologists who completed the intervention in the control group (n = 9). At T1, the intervention group had 12% higher odds of positive mammographic results compared to the controls, after controlling for baseline (odds ratio, 1.12; 95% confidence interval, 1.00-1.27; P = .0569). At T2, a similar association was found, but it was not statistically significant (odds ratio, 1.10; 95% confidence interval, 0.96 to 1.25). No associations were found among radiologists in the control group when comparing those who completed the continuing medical education intervention (n = 9) to those who did not (n = 10). In addition, no associations were found between time period and recall rate among radiologists who set realistic goals.ConclusionsThis study resulted in a null effect, which may indicate that a single 1-hour intervention is not adequate to change excessive recall among radiologists who undertook the intervention being tested.

Rationale and Objectives: The purpose of this study is to quantify breast radiologists' performance at predicting occult invasive disease when ductal carcinoma in situ (DCIS) presents as calcifications on mammography and to identify imaging and histopathological features that are associated with radiologists' performance.Materials and Methods: Mammographically detected calcifications that were initially diagnosed as DCIS on core biopsy and underwent definitive surgical excision between 2010 and 2015 were identified. Thirty cases of suspicious calcifications upstaged to invasive ductal carcinoma and 120 cases of DCIS confirmed at the time of definitive surgery were randomly selected. Nuclear grade, estrogen and progesterone receptor status, patient age, calcification long axis length, and breast density were collected. Ten breast radiologists who were blinded to all clinical and pathology data independently reviewed all cases and estimated the likelihood that the DCIS would be upstaged to invasive disease at surgical excision. Subgroup analysis was performed based on nuclear grade, long axis length, breast density and after exclusion of microinvasive disease.Results: Reader performance to predict upstaging ranged from an area under the receiver operating characteristic curve (AUC) of 0.541-0.684 with a mean AUC of 0.620 (95%CI: 0.489-0.751). Performances improved for lesions smaller than 2 cm (AUC: 0.676 vs 0.500; p = 0.002). The exclusion of microinvasive cases also improved performance (AUC: 0.651 vs 0.620; p = 0.005). There was no difference in performance based on breast density (p = 0.850) or nuclear grade (p = 0.270) CONCLUSION: Radiologists were able to predict invasive disease better than chance, particularly for smaller DCIS lesions (<2 cm) and after the exclusion of microinvasive disease. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To evaluate readability of commonly accessed online patient educational materials (OPEM) on breast density in setting of recently passed federal breast density legislation.Materials and Methods: The term "breast density" was queried using an online search engine to identify the top 50 commonly accessed websites based on order of search results on December 15, 2018. Location, cookies, and user account information were disabled prior to our query. Only websites with OPEM, defined as any educational material on breast density targeted towards the general public, were evaluated in our study. Sponsored hits and research journal articles were excluded. Available patient-directed information from websites meeting inclusion criteria was then downloaded. Grade-level readability was then determined from formatted content using generalized estimating equations, with observations nested within readability metrics from each website. Results were compared to American Medical Association recommended readability parameters (sixth-grade reading level). All interval estimates were calculated for 95% confidence.Results: Fouty-one websites met inclusion criteria representing patient-directed OPEM on breast density. Average grade-level readability of health information on breast density in our study ranged from 8.5-16.5 years with an average grade reading level of 11.1 years across all websites. Of websites fitting into a specific category, academic websites had the highest average grade reading level (12.0), while nonprofit websites had the lowest average grade reading level (10.4). Nearly half (19/41) of all websites in our study had diagrams to aid in patient comprehension, while few websites (2/41; 4.8%) displayed videos in addition to written content. The website with the lowest average grade reading level was WebMD, which had an average reading level of 8.5. No individual website in our study met American Medical Association recommended parameters of a sixth-grade reading level CONCLUSION: Readability of currently available OPEM on breast density may be written at a level too difficult for the general public to comprehend, which may represent a barrier to educational goals of newly passed federal breast density legislation. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To assess the extent of background parenchymal enhancement (BPE) at contrast-enhanced spectral mammography (CESM), association between clinical factors and BPE, and between BPE extent and breast cancer.Materials and Methods: This study included 516 women who underwent CESM imaging for screening and diagnostic purposes between 2012 and 2015 in a single center. BPE at CESM images was retrospectively, independently and blindly graded by six experienced radiologists using the following scale: minimal, mild, moderate, or marked. Agreement between readers was estimated using Kendall's W coefficient of concordance. Associations between clinical factors and BPE, and between BPE and breast cancer were examined using generalized estimating equations. Association between BPE and breast cancer was assessed for the whole study group, and for the screening population separately.Results: Most women underwent CESM for breast cancer screening (424/516, 82.2%). Mean age was 53 years, the majority had dense breasts (50.4-94%, depending on the reviewer), and minimal to mild BPE (75.8-89.9%). A total of 53/516 women had breast cancer. Overall concordance (W) values between the readers were 0.611 for breast density and 0.789 on BPE. Increased breast density and younger age were positive predictors for increased BPE (odds ratio [OR] 4.07, 95% confidence interval [CI] 2.32-7.14, p < 0.001; OR 2.88, 95% CI 1.87-4.42, p < 0.001, respectively). Breast radiation therapy was a negative predictor for BPE (OR 0.13, 95% CI 0.06-0.31, p < 0.001). Women with increased BPE had increased odds for breast cancer (OR 2.24, 95% CI 1.23-4.09, p = 0.008). This result was consistent when screening cases were analyzed separately (OR 6.27, 95% CI 2.38-16.53, p < 0.001).Conclusion: BPE at CESM was associated with breast density. Women with increased BPE had increased odds for breast cancer, independently of other potential risk factors. [ABSTRACT FROM AUTHOR]

Background: Breast Screen Reader Assessment Strategy (BREAST) is an innovative training and research program for radiologists in Australia and New Zealand. The aim of this study is to evaluate the efficacy of BREAST test sets in improving readers' performance in detecting cancers on mammograms.Materials and Methods: Between 2011 and 2018, 50 radiologists (40 fellows, 10 registrars) completed three BREAST test sets and 17 radiologists completed four test sets. Each test set contained 20 biopsy-proven cancer and 40 normal cases. Immediate image-based feedback was available to readers after they completed each test set which allowed the comparison of their selections with the truth. Case specificity, case sensitivity, lesion sensitivity, the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Jackknife Free-Response Receiver Operating Characteristic (JAFROC) Figure of Merit (FOM) were calculated for each reader. Kruskal-Wallis test was utilized to compare scores of the radiologist and registrars across all test-sets whilst Wilcoxon signed rank test was to compare the scores between pairs of test sets.Results: Significant improvements in lesion sensitivity ranging from 21% to 31% were found in radiologists completing later test sets compared to first test set (p ≤ 0.01). Eighty three percent of radiologists achieved higher performance in lesion sensitivity after they completed the first read. Registrars had significantly better scores in the third test set compared to the first set with mean increases of 79% in lesion sensitivity (p = 0.005) and 37% in JAFROC (p = 0.02). Sixty percent and 100% of registrars increased their scores in lesion sensitivity in the second and third reads compared to the first read while the percentage of registrars with higher scores in JAFROC was 80%.Conclusion: Introduction of BREAST into national training programs appears to have an important impact in promoting diagnostic efficacy amongst radiologists and radiology registrars undergoing mammographic readings. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To assess the feasibility of using an art history tool of formal analysis in resident education for interpretations of mammography and chest radiographs METHODS: In a pre-post study design, residents were shown pre-selected 10 mammograms and 10 chest radiographs for a total of 20 unique anonymized patient cases. After the pretest, residents attended four formal analysis art history lessons. The formal elements included line, light, dark, shade, proportion and balance. The post-tests were administered utilizing the same set of images given during the pre-test. After the completion of the pre- and post-tests, the participants filled out a written survey., Results: On average, participants improved their image descriptions for a mean of 30% of the total number of mammographic images they evaluated, and similarly they improved their image diagnoses for a mean of 31% of the mammographic images. On average, participants improved their image descriptions for a mean of 37% of the total number of chest radiographs they evaluated, while improving their image diagnoses for a mean of 52% of the chest X-rays. Of the 20 participants, 14 (70%) completed the post study survey. Almost all of the respondents endorsed agreement in understanding the application of formal analysis to radiologic interpretation. Eight out of 14 (57%) participants self-reported improvement in identifying abnormalities and contriving descriptors., Conclusion: This pilot study shows that formal art analysis used by art historians may improve radiologic learners' ability to perceive and describe relevant radiologic abnormalities which in turn would lead to a more accurate radiologic differential diagnosis. The formal analysis process trains the eye to detect and categorize the underlying structure of images. This method provides an alternative arts intervention specifically designed to improve fundamental visual skills in radiology education., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: The purpose of this study was to compare the utilization of the Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment in patients recalled from screening before and after the implementation of digital breast tomosynthesis (DBT).Materials and Methods: This was a retrospective review of 11,478 digital mammography (DM) screening exams and 9350 DM+DBT screening exams. Lesions assigned a BI-RADS category 3 at diagnostic exam were classified as architectural distortions, asymmetries, calcifications, masses, and "other" and followed for a minimum of 2 years.Results: The addition of DBT to screening DM resulted in a 30.4% relative reduction (10.3 women per 1000) in the utilization of BI-RADS category 3 compared to screening DM alone (3.4% for DM versus 2.4% for DM+DBT; p < 0.0001). There was a statistically significant change in the distribution of category 3 findings with DM+DBT characterized by an increase in calcifications and architectural distortions and a decrease in asymmetries. There was no change in category 3 assessment for masses. Although both cohorts had delayed cancer detection rates that exceeded the recommended 2% benchmark (2.3% for DM and 3.6% for DM+DBT), when limited to invasive malignancies, the delayed cancer detection rates were below the 2% benchmark (1.5% for DM and 0.9% for DM+DBT). Screening DM+DBT resulted in a 9.2% relative reduction in recall rate compared to DM (13.0% for DM versus 11.8% for DM+ DBT, p = 0.012).Conclusion: Implementation of DBT in the screening population decreased the overall number of patients assigned to short-term follow-up by 10.3 per 1000 women while maintaining comparable rates of delayed cancer detection. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.Materials and Methods: A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.Results: In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.Conclusion: The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To evaluate the reliability of cone-beam breast computed tomography (CBBCT) in visual assessment of breast density referring to the fifth edition of the Breast Imaging Reporting and Data System compared to digital mammography.Materials and Methods: Breast density assessments of 130 female patients were performed by five radiologists referring to the fifth edition of Breast Imaging Reporting and Data System atlas both on two-view mammograms and CBBCT images. Assessments were repeated by three radiologists with different experience more than 1 month after the initial evaluation. The inter- and intrareader agreements were compared by using the Cohen's weighted Kappa statistic and intraclass correlation coefficient. Weighted Kappa statistic was also used to analyze the agreement between CBBCT images and mammograms. The influence of radiologist experience for breast density assessment was analyzed using a chi-square test.Results: For CBBCT images, the inter-reader agreement was 0.781, whereas the agreement on mammograms was 0.744, both demonstrating moderate agreement. The level of intrareader reliability was higher on the CBBCT images than mammograms for breast density evaluation, 0.856 versus 0.786. Based on the majority report, the agreement between these two modalities was on substantial agreement degree. There was a statistically significant difference among radiologists with different levels of experience, and higher density categories were reported more often by experienced reader.Conclusion: CBBCT showed equal aptitude and better agreement for the breast density evaluation compared to mammography. CBBCT could be an effective modality for breast density assessment and breast cancer risk evaluation in routine diagnosis and breast cancer screening. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Ductal carcinoma in situ (DCIS) hinders imaging detection due to multifocal appearance and discontinuous growth. Preoperative determination of its extent is therefore challenging. Aim of this study was to investigate the additional benefit of breast magnetic resonance imaging (MRI) to mammography (MG) in the diagnosis of DCIS according to size and grading.Materials and Methods: Retrospective analysis of 295 patients with biopsy-proven, pure DCIS. Mean patient age was 57.0 years (27-87 years). All patients obtained MG. Additional MRI was performed in 41.7% (123/295). Mammographic breast density, background parenchymal enhancement (BPE), tumor size and grading were analysed. Tumor size on MG and MRI were compared to histopathological size of the surgical specimen.Results: Mean tumor size was 39.6 mm. DCIS was occult on MG in 24.4% (30/123) and on MRI in 1.6% (2/123). Size was underestimated by 4.6 mm (mean) mammographically. DCIS was high grade in 54.5% (67/123), intermediate grade in 40.7% (50/123) and low grade in 4.9% (6/123). MG was exact regarding tumor size in low grade DCIS, underestimated intermediate grade DCIS by 1 mm (median) and high grade DCIS by 10.5 mm. MRI overestimated low grade DCIS by 1 mm (median), was exact regarding intermediate grade DCIS and underestimated high grade DCIS by 1 mm. BPE did not influence tumor detection and measurement.Conclusion: MRI outperforms MG in the detection and size estimation of DCIS and can reduce positive margin rates. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammography (FFDM).Materials and Methods: In this study, we retrospectively collected FFDM and DBT images of 78 biopsy-proven lesions from 76 patients. A region of interest was selected for each lesion on FFDM, synthesized 2D, and DBT key slice images. Features were extracted from each lesion using a pretrained convolutional neural network (CNN) and served as input to a support vector machine classifier trained in the task of predicting likelihood of malignancy.Results: From receiver operating characteristic (ROC) analysis of all 78 lesions, the synthesized 2D image performed best in both the cradiocaudal view (area under the ROC curve [AUC] = 0.81, SE = 0.05) and mediolateral oblique view (AUC = 0.88, SE = 0.04) in the task of lesion characterization. When cradiocaudal and mediolateral oblique data of each lesion were merged through soft voting, DBT key slice image performed best (AUC = 0.89, SE = 0.04). When only masses and architectural distortions (ARDs) were considered, DBT performed significantly better than FFDM (p = 0.024).Conclusion: DBT performed significantly better than FFDM in the merged view classification of mass and ARD lesions. The increased performance suggests that the information extracted by the CNN from DBT images may be more relevant to lesion malignancy status than the information extracted from FFDM images. Therefore, this study provides supporting evidence for the efficacy of computer-aided diagnosis on DBT in the evaluation of mass and ARD lesions. [ABSTRACT FROM AUTHOR]