Patient Preferences of Breast Radiologists' Attire and Appearance.

RATIONALE AND OBJECTIVES: To assess patient preferences for breast radiologists' attire and appearance. MATERIALS AND METHODS: A multi-institutional anonymous, voluntary 19-question survey was administered to patients undergoing screening and diagnostic mammography examinations over a 5-week period. Using a 5-point Likert scale, respondents were asked about their preferences for gender-neutral attire (white coat), male-presenting attire (scrubs, dress shirt with tie, or dress shirt without tie), and female-presenting attire (scrubs, dress, blouse with pants, and blouse with skirt). Patient responses were compared to demographic data using bivariable analysis and multivariable regression. RESULTS: Response rate was 84.7% (957/1130). Mean respondent age was 57.2 years±11.9. Most respondents agreed/strongly agreed that the breast radiologist's appearance mattered (52.5%, 502/956) followed by being indifferent (28.1%, 269/956). Respondents with greater education levels felt less strongly (p=0.001) about the radiologist's appearance: 63.3% (70/110) less than college cared about appearance compared to 53.5% (266/497) college/vocational and 47.4% (165/348) graduate. Most respondents felt indifferent about a breast radiologist wearing a white coat (68.9%, 657/954) or about male-presenting breast radiologists wearing a tie (77.1%, 734/952) without significant demographic differences. Almost all respondents either prefer/strongly prefer (60.1%, 572/951) or were indifferent (39.6%, 377/951) to all breast radiologists wearing scrubs when performing procedures. While respondents approved of all attire choices overall, most respondents preferred scrubs for both male- and female-presenting breast radiologists (64.0%, 612/957 and 64.9%, 621/957, respectively). CONCLUSION: A variety of breast radiologists' attire can be worn while maintaining provider professionalism and without compromising patient expectations.

Impact of Interruptions During Screening Mammography on Physician Well-Being and Patient Care.

OBJECTIVE: To assess the impact of interruptions on radiologists' efficiency, accuracy, and job satisfaction in interpreting screening mammograms. METHODS: This institutional review board-approved retrospective reader study recruited nine breast radiologists from a single academic institution [name withheld] to interpret 150 screening mammograms performed between December 1, 2008, and December 31, 2015 under two different reading conditions, as follows: (1) uninterrupted batch reading and (2) interrupted reading. The 150 cases consisted of 125 normal mammograms and 25 mammograms with subtle breast cancers. Cases were divided into two groups of 75 cases each (cohort 1 and cohort 2), with a comparable distribution of cancer cases. Four rounds of 75 cases each were conducted with a 6-week washout period between rounds 2 and 3. After completing each interpretation session, readers completed a seven-question survey, assessing perceptions of mental and physical effort, level of frustration, and performance satisfaction. Clinical performance metrics (reading time, recall rate, sensitivity, specificity, accuracy, and positive predictive value 1) were calculated. RESULTS: Recall rates were significantly (P = .04) higher during interrupted reading sessions (35.4%) than they were during uninterrupted batch reading sessions (31.4%). Accuracy was significantly (P = .049) worse in the interrupted reading sessions (69.5%), compared with uninterrupted sessions (73.6%). Differences in overall image interpretation times were not statistically significant (P = .065). Compared with uninterrupted batch reading sessions, readers during interrupted sessions reported feeling busier (P < .001), encountered higher levels of cognitive demand (P = .005), experienced elevated levels of physical fatigue (P = .004), and expressed lower levels of satisfaction with their performance (P = .041). CONCLUSION: Interruptions during interpretation of screening mammography have deleterious effects on physician performance and their sense of well-being.

Racial Disparities in Breast Imaging Wait Times Before and After the Implementation of a Same-Day Biopsy Program.

OBJECTIVE: To examine time from screening to diagnostic workup, biopsy, and surgery for non-Hispanic White (NHW) and Black women following implementation of a same-day biopsy program. METHODS: All NHW and Black women with BI-RADS category 0 screening mammogram at Duke University Hospital were identified between August 1, 2020, and August 1, 2021. Patient characteristics were recorded. Time between screening mammogram, diagnostic workup, breast biopsy, surgical consultation, and surgery were recorded. Comparisons were made between NHW and Black women using a multivariable regression model. Diagnostic imaging to biopsy time interval was compared to historical averages before same-day biopsy implementation. RESULTS: There were 2156 women: 69.9% NHW (1508/2156) and 30.1% Black (648/2156). Mean ± standard deviation time from screening to diagnostic imaging overall was 13.5 ±â€…32.5 days but longer for Black (18.0 ±â€…48.3 days) than for NHW women (11.5 ±â€…22.2 days) (P < 0.001). The mean time from diagnostic mammogram to biopsy was 5.9 ±â€…18.9 days, longer for Black (9.0 ±â€…27.9 days) than for NHW women (4.4 ±â€…11.8 days) (P = 0.017). The same-day biopsy program shortened the time from diagnostic imaging to biopsy overall (12.5 ±â€…12.4 days vs 5.9 ±â€…18.9 days; P < 0.001), with a significant reduction for NHW women (12.4 ±â€…11.7 days vs 4.4 ±â€…11.8 days) (P < 0.001) but not Black women (11.5 ±â€…9.9 days vs 9.0 ±â€…27.9 days) (P = 0.527). CONCLUSION: Disparities exist along the breast imaging pathway. A same-day biopsy program benefited NHW women more than Black women.

Ethanol Sclerotherapy for Postoperative Seroma of the Breast and Axilla.

OBJECTIVE: Evaluate the effectiveness of alcohol sclerotherapy in postoperative breast and axillary seromas. METHODS: This was an IRB-approved retrospective review of consecutive patients from 2017 to 2021. The procedure involves aspiration of seroma fluid, injection of ethanol for 15 to 30 minutes, and then aspiration of the injected ethanol. Following review of the medical record, patient and procedure data were recorded. Success was defined as no recurrence of seroma. Statistical analysis was performed using a chi-square or t-test, as appropriate. RESULTS: Twenty seromas were treated in 19 patients (mean age, 63 years; range, 49-79) following mastectomy (9/20, 45%), lumpectomy (25%, 5/20), axillary nodal dissection (5/20, 25%), or chest wall surgery (1/20, 5%). The mean seroma volume was 146 ±â€…165 mL. There was a mean of 1.4 prior aspiration attempts (range, 0-3). Mean injected ethanol volume was 39 mL (range, 8-60 mL) for a mean of 26 ±â€…5 minutes. Seromas recurred in 85% (17/20) of cases, and repeat aspiration was performed in 35% (6/17). The mean recurrence time was 34 ±â€…29 days. The recurrent seromas were 31% smaller (mean, 80 ±â€…48 mL). Only a smaller pre-ablation seroma was associated with successful ablation (P < 0.01), as the mean pre-ablation volume was 36 ± 24 mL for successful ablations and 119 ±â€…56 mL for unsuccessful ablations. There were no post-procedural complications. CONCLUSION: Single-treatment ethanol sclerotherapy is typically unsuccessful in ablating breast and axillary seromas. Only a smaller pre-ablation seroma size was associated with successful ablation.

Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.

BACKGROUND. The incidence of ductal carcinoma in situ (DCIS) has steadily increased, as have concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Radiologists are not typically expected to predict the upstaging of DCIS to invasive disease, though they might be trained to perform this task. OBJECTIVE. The purpose of this study was to determine whether a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. METHODS. All cases of DCIS calcifications that underwent stereotactic biopsy between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases (120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery). Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their ability to predict upstaging. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performances between rounds 1 and 2. RESULTS. The mean AUC for reader performance in predicting upstaging in round 1 was 0.623 (range, 0.514-0.684). In the focus group, radiologists agreed that upstaging was better predicted when an associated mass, asymmetry, or architectural distortion was present; when densely packed calcifications extended over a larger area; and when the most suspicious features were focused on rather than the most common features. Additionally, radiologists agreed that BI-RADS descriptors do not adequately characterize risk of invasion, and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. Reader performance significantly improved in round 2 (mean AUC, 0.765; range, 0.617-0.852; p = .045). CONCLUSION. A mixed-methods two-stage observer study identified factors that helped radiologists significantly improve their ability to predict upstaging of DCIS to invasive disease. CLINICAL IMPACT. Breast radiologists can be trained to better predict upstaging of DCIS to invasive disease, which may facilitate discussions with patients and referring providers.

Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance.

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.

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development. PURPOSE: To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer. STUDY TYPE: Case-control study. POPULATION: In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years. FIELD STRENGTH/SEQUENCE: 5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences. ASSESSMENT: Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available. STATISTICAL TESTS: Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared. RESULTS: The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers. DATA CONCLUSION: Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk.

RATIONALE AND OBJECTIVES: To determine if background parenchymal enhancement (BPE) on screening breast magnetic resonance imaging (MRI) in high-risk women correlates with future cancer. MATERIALS AND METHODS: All screening breast MRIs (n = 1039) in high-risk women at our institution from August 1, 2004, to July 30, 2013, were identified. Sixty-one patients who subsequently developed breast cancer were matched 1:2 by age and high-risk indication with patients who did not develop breast cancer (n = 122). Five fellowship-trained breast radiologists independently recorded the BPE. The median reader BPE for each case was calculated and compared between the cancer and control cohorts. RESULTS: Cancer cohort patients were high-risk because of a history of radiation therapy (10%, 6 of 61), high-risk lesion (18%, 11 of 61), or breast cancer (30%, 18 of 61); BRCA mutation (18%, 11 of 61); or family history (25%, 15 of 61). Subsequent malignancies were invasive ductal carcinoma (64%, 39 of 61), ductal carcinoma in situ (30%, 18 of 61) and invasive lobular carcinoma (7%, 4of 61). BPE was significantly higher in the cancer cohort than in the control cohort (P = 0.01). Women with mild, moderate, or marked BPE were 2.5 times more likely to develop breast cancer than women with minimal BPE (odds ratio = 2.5, 95% confidence interval: 1.3-4.8, P = .005). There was fair interreader agreement (κ = 0.39). CONCLUSIONS: High-risk women with greater than minimal BPE at screening MRI have increased risk of future breast cancer.

Anxiety prior to breast biopsy: Relationships with length of time from breast biopsy recommendation to biopsy procedure and psychosocial factors.

This study investigated how time from breast biopsy recommendation to biopsy procedure affected pre-biopsy anxiety ( N = 140 women), and whether the relationship between wait time and anxiety was affected by psychosocial factors (chronic life stress, traumatic events, social support). Analyses showed a significant interaction between wait time and chronic life stress. Increased time from biopsy recommendation was associated with greater anxiety in women with low levels of life stress. Women with high levels of life stress experienced increased anxiety regardless of wait time. These results suggest that women may benefit from shorter wait times and receiving strategies for managing anxiety.

Imaging-Guided Core-Needle Breast Biopsy: Impact of Meditation and Music Interventions on Patient Anxiety, Pain, and Fatigue.

PURPOSE: To evaluate the impact of guided meditation and music interventions on patient anxiety, pain, and fatigue during imaging-guided breast biopsy. METHODS: After giving informed consent, 121 women needing percutaneous imaging-guided breast biopsy were randomized into three groups: (1) guided meditation; (2) music; (3) standard-care control group. During biopsy, the meditation and music groups listened to an audio-recorded, guided, loving-kindness meditation and relaxing music, respectively; the standard-care control group received supportive dialogue from the biopsy team. Immediately before and after biopsy, participants completed questionnaires measuring anxiety (State-Trait Anxiety Inventory Scale), biopsy pain (Brief Pain Inventory), and fatigue (modified Functional Assessment of Chronic Illness Therapy-Fatigue). After biopsy, participants completed questionnaires assessing radiologist-patient communication (modified Questionnaire on the Quality of Physician-Patient Interaction), demographics, and medical history. RESULTS: The meditation and music groups reported significantly greater anxiety reduction (P values < .05) and reduced fatigue after biopsy than the standard-care control group; the standard-care control group reported increased fatigue after biopsy. The meditation group additionally showed significantly lower pain during biopsy, compared with the music group (P = .03). No significant difference in patient-perceived quality of radiologist-patient communication was noted among groups. CONCLUSIONS: Listening to guided meditation significantly lowered biopsy pain during imaging-guided breast biopsy; meditation and music reduced patient anxiety and fatigue without compromising radiologist-patient communication. These simple, inexpensive interventions could improve women's experiences during core-needle breast biopsy.

Frequency of Malignancy and Imaging Characteristics of Probably Benign Lesions Seen at Breast MRI.

OBJECTIVE: The purposes of this study were to evaluate the frequency, follow-up compliance, and cancer rate of MRI BI-RADS category 3 lesions and to determine the cancer rate for individual BI-RADS descriptors. MATERIALS AND METHODS: A retrospective review was conducted of breast MRI examinations with an assessment of probably benign (BI-RADS category 3) from among 4279 consecutive breast MRI examinations performed from January 2005 through December 2009. The review revealed 282 (6.6%) examinations with 332 lesions defined as BI-RADS 3. Pathologic results, 2 years of follow-up imaging findings, or both were reviewed. The frequency of BI-RADS 3 assessments, follow-up imaging compliance, and cancer yield were calculated. Three fellowship-trained breast imagers reevaluated all lesions and recorded descriptors from the MRI lexicon of the fifth edition of the BI-RADS atlas. The distribution and likelihood of malignancy for each descriptor were calculated. RESULTS: The follow-up compliance rate was 84.3% (280/332), and the malignancy rate was 4.3% (12/280). There were 50 (17.9%) individual foci, 61 (21.8%) multiple foci, 74 (26.4%) masses, and 95 (33.9%) nonmass enhancement lesions. Masses were most commonly oval (59.5% [44/74]), circumscribed (75.7% [56/74]), and homogeneously enhancing (43.2% [32/74]). Nonmass enhancement was most commonly focal (57.9% [55/95]) with heterogeneous enhancement (53.7% [51/95]) Most of the lesions had persistent kinetics (74.3% [208/280]). The background parenchymal enhancement was most commonly mild (51.1% [143/280]). CONCLUSION: MRI BI-RADS category 3 is not frequently used, and the levels of patient compliance with follow-up imaging are acceptable. The cancer yield for probably benign lesions is greater for MRI-detected than for mammographically detected lesions, especially for specific BI-RADS descriptors.

Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.

PURPOSE: The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms. METHODS: In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status. RESULTS: The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024). CONCLUSION: Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.

Interobserver Variability Between Breast Imagers Using the Fifth Edition of the BI-RADS MRI Lexicon.

OBJECTIVE: The purpose of this study was to assess the interobserver variability of users of the MRI lexicon in the fifth edition of the BI-RADS atlas. MATERIALS AND METHODS: Three breast imaging specialists reviewed 280 routine clinical breast MRI findings reported as BI-RADS category 3. Lesions reported as BI-RADS 3 were chosen because variability in the use of BI-RADS descriptors may influence which lesions are classified as probably benign. Each blinded reader reviewed every study and recorded breast features (background parenchymal enhancement) and lesion features (lesion morphology, mass shape, mass margin, mass internal enhancement, nonmass enhancement distribution, nonmass enhancement internal enhancement, enhancement kinetics) according to the fifth edition of the BI-RADS lexicon and provided a final BI-RADS assessment. Interobserver variability was calculated for each breast and lesion feature and for the final BI-RADS assessment. RESULTS: Interobserver variability for background parenchymal enhancement was fair (ĸ = 0.28). There was moderate agreement on lesion morphology (ĸ = 0.53). For masses, there was substantial agreement on shape (ĸ = 0.72), margin (ĸ = 0.78), and internal enhancement (ĸ = 0.69). For nonmass enhancement, there was substantial agreement on distribution (ĸ = 0.69) and internal enhancement (ĸ = 0.62). There was slight agreement on lesion kinetics (ĸ = 0.19) and final BI-RADS assessment (ĸ = 0.11). CONCLUSION: There is moderate to substantial agreement on most MRI BI-RADS lesion morphology descriptors, particularly mass and nonmass enhancement features, which are important predictors of malignancy. Considerable disagreement remains, however, among experienced readers whether to follow particular findings.

Whole Breast Ultrasound: Comparison of the Visibility of Suspicious Lesions with Automated Breast Volumetric Scanning Versus Hand-Held Breast Ultrasound.

RATIONALE AND OBJECTIVES: To assess how well radiologists visualize relevant features of lesions seen with automated breast volumetric scanning (ABVS) in comparison to hand-held breast ultrasound in women going to breast biopsy. MATERIALS AND METHODS: Twenty-five subjects were recruited from women who were scheduled to undergo a breast biopsy for at least one Breast Imaging-Reporting and Data System four or five lesion identified in a diagnostic setting. In this institutional review board-approved study, the subjects underwent imaging of the breast(s) of concern using a dedicated system that allowed both hand-held breast ultrasound and ABVS. Five experienced breast radiologists reviewed the 30 lesions in 25 subjects in a reader study. Each reader was asked to specify the lesion type, size, imaging features, Breast Imaging-Reporting and Data System, and suspicion of malignancy and to compare the lesion characteristics of shape and margins between the two modalities. RESULTS: Seven (23.3%) masses were malignant and 23 (76.4%) were benign. Across all lesions regardless of size or final pathology, there was no significant difference in sensitivity or specificity (P > .15) between the two modalities. For malignant lesions, the reader visualization confidence scores between the two ultrasound modalities were not significantly different (P > .1). However, analysis for nonmalignant cases showed a statistically significant increase in reader visualization confidence in lesion shape and margins (P < .001). CONCLUSIONS: Radiologists showed increased confidence in visualization of benign masses and equal confidence in suspicious masses with ABVS imaging. This information could help decrease the need for additional hand-held imaging after automated whole breast ultrasound.

Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.

PURPOSE: Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure the highest benefit of screening mammography for patients. The authors have previously proposed a computer-aided education system based on trainee models. Those models relate human-assessed image characteristics to trainee error. In this study, the authors propose to build trainee models that utilize features automatically extracted from images using computer vision algorithms to predict likelihood of missing each mass by the trainee. This computer vision-based approach to trainee modeling will allow for automatically searching large databases of mammograms in order to identify challenging cases for each trainee. METHODS: The authors' algorithm for predicting the likelihood of missing a mass consists of three steps. First, a mammogram is segmented into air, pectoral muscle, fatty tissue, dense tissue, and mass using automated segmentation algorithms. Second, 43 features are extracted using computer vision algorithms for each abnormality identified by experts. Third, error-making models (classifiers) are applied to predict the likelihood of trainees missing the abnormality based on the extracted features. The models are developed individually for each trainee using his/her previous reading data. The authors evaluated the predictive performance of the proposed algorithm using data from a reader study in which 10 subjects (7 residents and 3 novices) and 3 experts read 100 mammographic cases. Receiver operating characteristic (ROC) methodology was applied for the evaluation. RESULTS: The average area under the ROC curve (AUC) of the error-making models for the task of predicting which masses will be detected and which will be missed was 0.607 (95% CI,0.564-0.650). This value was statistically significantly different from 0.5 (p<0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p=0.0009). Therefore, generally the authors' models were able to predict which masses were detected and which were missed better than chance. CONCLUSIONS: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner.

Predictors of pain experienced by women during percutaneous imaging-guided breast biopsies.

PURPOSE: The purpose of this study was to evaluate pain experienced during imaging-guided core-needle breast biopsies and to identify factors that predict increased pain perception during procedures. METHODS: In this institutional review board-approved, HIPAA-compliant protocol, 136 women undergoing stereotactically or ultrasound-guided breast biopsy or cyst aspiration were recruited and provided written informed consent. Participants filled out questionnaires assessing anticipated biopsy pain, ongoing breast pain, pain experienced during biopsy, catastrophic thoughts about pain during biopsy, anxiety, perceived communication with the radiologist, chronic life stress, and demographic and medical information. Procedure type, experience level of the radiologist performing the biopsy, number of biopsies, breast density, histology, and tumor size were recorded for each patient. Data were analyzed using Spearman's ρ correlations and a probit regression model. RESULTS: No pain (0 out of 10) was reported by 39.7% of women, mild pain (1-3 out of 10) by 48.5%, and moderate to severe pain (≥4 out of 10) by 11.8% (n = 16). Significant (P < .05) predictors of greater biopsy pain in the probit regression model included younger age, greater prebiopsy breast pain, higher anticipated biopsy pain, and undergoing a stereotactic procedure. Anticipated biopsy pain correlated most strongly with biopsy pain (ß = .27, P = .004). CONCLUSIONS: Most patients report minimal pain during imaging-guided biopsy procedures. Women experiencing greater pain levels tended to report higher anticipated pain before the procedure. Communication with patients before biopsy regarding minimal average pain reported during biopsy and encouragement to make use of coping strategies may reduce patient anxiety and anticipated pain.

Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.

PURPOSE: To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. MATERIALS AND METHODS: Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). RESULTS: There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. CONCLUSION: The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma.

Radiology resident mammography training: interpretation difficulty and error-making patterns.

RATIONALE AND OBJECTIVES: The purpose of this study was to better understand the concept of mammography difficulty and how it affects radiology resident performance. MATERIALS AND METHODS: Seven radiology residents and three expert breast imagers reviewed 100 mammograms, consisting of bilateral medial lateral oblique and craniocaudal views, using a research workstation. The cases consisted of normal, benign, and malignant findings. Participants identified abnormalities and scored the difficulty and malignant potential for each case. Resident performance (sensitivity, specificity, and area under the receiver operating characteristic curve [AUC]) was calculated for self- and expert-assessed high and low difficulties. RESULTS: For cases classified by self-assessed difficulty, the resident AUCs were 0.667 for high difficulty and 0.771 for low difficulty cases (P = .010). Resident sensitivities were 0.707 for high and 0.614 for low difficulty cases (P = .113). Resident specificities were 0.583 for high and 0.905 for low difficulty cases (P < .001). For cases classified by expert-assessed difficulty, the resident AUCs were 0.583 for high and 0.783 for low difficulty cases (P = .001). Resident sensitivities were 0.558 for high and 0.796 for low difficulty cases (P < .001). Resident specificities were 0.714 for high and 0.740 for low difficulty cases (P = .807). CONCLUSIONS: Increased self- and expert-assessed difficulty is associated with a decrease in resident performance in mammography. However, while this lower performance is due to a decrease in specificity for self-assessed difficulty, it is due to a decrease in sensitivity for expert-assessed difficulty. These trends suggest that educators should provide a mix of self- and expert-assessed difficult cases in educational materials to maximize the effect of training on resident performance and confidence.

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.

PURPOSE: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. METHODS: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). RESULTS: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502-0.739, 95% Confidence Interval: 0.543-0.680,p < 0.002). CONCLUSIONS: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

Cancer cases from ACRIN digital mammographic imaging screening trial: radiologist analysis with use of a logistic regression model.

PURPOSE: To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. MATERIALS AND METHODS: This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. RESULTS: Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. CONCLUSION: The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.