Screening Mammography Interpretation Research Articles

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

ObjectiveTo assess the impact of interruptions on radiologists’ efficiency, accuracy, and job satisfaction in interpreting screening mammograms. MethodsThis 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. ResultsRecall 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). ConclusionInterruptions during interpretation of screening mammography have deleterious effects on physician performance and their sense of well-being.

Breast Cancer Screening Interpretation Model: An Opportunity for Optimization of Patient and Practice Outcomes.

Identifying best practices in screening mammography interpretation is critical for maximizing early detection while minimizing false-positive results and patient anxiety. Factors impacting mammography outcomes are variable and include physician characteristics, patient characteristics, practice setting, and models of mammography interpretation [1]. The different screening mammography interpretation practice patterns include uninterrupted batch screening, which could be performed online or live (screening mammograms are interpreted in batches while patients are waiting with or without subsequent diagnostic examinations performed on the same day), or offline (screening mammograms are read after patients leave and recalled patients return on a separate diagnostic appointment), or interrupted (screening mammograms are read in between other breast imaging tasks, including diagnostic examinations or interventional procedures), which also could be live or offline [2].

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence

With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings. To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population. In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammography Challenge, was used to predict the cancer status of an individual using a standard set of screening mammography images. This study was conducted using retrospective patient data collected between 2010 and 2020 from women aged 40 years and older who underwent a routine breast screening examination and participated in the Athena Breast Health Network at the University of California, Los Angeles (UCLA). Performance of the challenge ensemble method (CEM) and the CEM combined with radiologist assessment (CEM+R) were compared with diagnosed ductal carcinoma in situ and invasive cancers within a year of the screening examination using performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Evaluated on 37 317 examinations from 26 817 women (mean [SD] age, 58.4 [11.5] years), individual model AUROC estimates ranged from 0.77 (95% CI, 0.75-0.79) to 0.83 (95% CI, 0.81-0.85). The CEM model achieved an AUROC of 0.85 (95% CI, 0.84-0.87) in the UCLA cohort, lower than the performance achieved in the Kaiser Permanente Washington (AUROC, 0.90) and Karolinska Institute (AUROC, 0.92) cohorts. The CEM+R model achieved a sensitivity (0.813 [95% CI, 0.781-0.843] vs 0.826 [95% CI, 0.795-0.856]; P = .20) and specificity (0.925 [95% CI, 0.916-0.934] vs 0.930 [95% CI, 0.929-0.932]; P = .18) similar to the radiologist performance. The CEM+R model had significantly lower sensitivity (0.596 [95% CI, 0.466-0.717] vs 0.850 [95% CI, 0.766-0.923]; P < .001) and specificity (0.803 [95% CI, 0.734-0.861] vs 0.945 [95% CI, 0.936-0.954]; P < .001) than the radiologist in women with a prior history of breast cancer and Hispanic women (0.894 [95% CI, 0.873-0.910] vs 0.926 [95% CI, 0.919-0.933]; P = .004). This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.

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

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

Advancements in Oncology with Artificial Intelligence-A Review Article.

Simple SummaryWith the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research.Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review.

The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. After data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n= 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n= 6]), retrospective simulation studies (38% [n= 5]), or both (15% [n= 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n= 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n= 9]) and the reference standard (69% [n= 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage. To date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.

Online or Offline: Does It Matter? A Review of Existing Interpretation Approaches and Their Effect on Screening Mammography Metrics, Patient Satisfaction, and Cost.

The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.

Radiologist Characteristics Associated with Interpretive Performance of Screening Mammography: A National Mammography Database (NMD) Study.

Background Factors affecting radiologists' performance in screening mammography interpretation remain poorly understood. Purpose To identify radiologists characteristics that affect screening mammography interpretation performance. Materials and Methods This retrospective study included 1223 radiologists in the National Mammography Database (NMD) from 2008 to 2019 who could be linked to Centers for Medicare & Medicaid Services (CMS) datasets. NMD screening performance metrics were extracted. Acceptable ranges were defined as follows: recall rate (RR) between 5% and 12%; cancer detection rate (CDR) of at least 2.5 per 1000 screening examinations; positive predictive value of recall (PPV1) between 3% and 8%; positive predictive value of biopsies recommended (PPV2) between 20% and 40%; positive predictive value of biopsies performed (PPV3) between the 25th and 75th percentile of study sample; invasive CDR of at least the 25th percentile of the study sample; and percentage of ductal carcinoma in situ (DCIS) of at least the 25th percentile of the study sample. Radiologist characteristics extracted from CMS datasets included demographics, subspecialization, and clinical practice patterns. Multivariable stepwise logistic regression models were performed to identify characteristics independently associated with acceptable performance for the seven metrics. The most influential characteristics were defined as those independently associated with the majority of the metrics (at least four). Results Relative to radiologists practicing in the Northeast, those in the Midwest were more likely to achieve acceptable RR, PPV1, PPV2, and CDR (odds ratio [OR], 1.4-2.5); those practicing in the West were more likely to achieve acceptable RR, PPV2, and PPV3 (OR, 1.7-2.1) but less likely to achieve acceptable invasive CDR (OR, 0.6). Relative to general radiologists, breast imagers were more likely to achieve acceptable PPV1, invasive CDR, percentage DCIS, and CDR (OR, 1.4-4.4). Those performing diagnostic mammography were more likely to achieve acceptable PPV1, PPV2, PPV3, invasive CDR, and CDR (OR, 1.9-2.9). Those performing breast US were less likely to achieve acceptable PPV1, PPV2, percentage DCIS, and CDR (OR, 0.5-0.7). Conclusion The geographic location of the radiology practice, subspecialization in breast imaging, and performance of diagnostic mammography are associated with better screening mammography performance; performance of breast US is associated with lower performance. ©RSNA, 2021 Online supplemental material is available for this article.

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC=0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC=0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.

Transitioning to Practice: Getting up to Speed in Efficiency and Accuracy.

The transition from trainee to breast radiologist is challenging. The many new responsibilities that breast radiologists acquire while establishing themselves as clinicians may increase stress and anxiety. Taking inventory of existing knowledge and skills and addressing deficits toward the end of one's training can be beneficial. New breast radiologists should expect to be slower and gain proficiency in the first several years out of training. Having realistic expectations for oneself with respect to screening mammography interpretation and following up on the subsequent diagnostic imaging workup of screening callback examinations can increase competence and confidence. Familiarity with the available literature to guide management in the diagnostic setting can increase efficiency. Planning ahead for localizations and biopsies also allows for efficiency while alleviating anxiety. Ultimately, adapting to a new work environment using a collaborative approach with primary healthcare providers, pathologists, and surgeons while remembering to have mentors within and beyond the field of radiology allows for a more successful transition.

Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.

Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.

Influences of Radiology Trainees on Screening Mammography Interpretation

PurposeParticipation of radiology trainees in screening mammographic interpretation is a critical component of radiology residency and fellowship training. The aim of this study was to investigate and quantify the effects of trainee involvement on screening mammographic interpretation and diagnostic outcomes. MethodsScreening mammograms interpreted at an academic medical center by six dedicated breast imagers over a three-year period were identified, with cases interpreted by an attending radiologist alone or in conjunction with a trainee. Trainees included radiology residents, breast imaging fellows, and fellows from other radiology subspecialties during breast imaging rotations. Trainee participation, patient variables, results of diagnostic evaluations, and pathology were recorded. ResultsA total of 47,914 mammograms from 34,867 patients were included, with an overall recall rate for attending radiologists reading alone of 14.7% compared with 18.0% when involving a trainee (P < .0001). Overall cancer detection rate for attending radiologists reading alone was 5.7 per 1,000 compared with 5.2 per 1,000 when reading with a trainee (P = .517). When reading with a trainee, dense breasts represented a greater portion of recalls (P = .0001), and more frequently, greater than one abnormality was described in the breast (P = .013). Detection of ductal carcinoma in situ versus invasive carcinoma or invasive cancer type was not significantly different. The mean size of cancers in patients recalled by attending radiologists alone was smaller, and nodal involvement was less frequent, though not statistically significantly. ConclusionsThese results demonstrate a significant overall increase in recall rate when interpreting screening mammograms with radiology trainees, with no change in cancer detection rate. Radiology faculty members should be aware of this potentiality and mitigate tendencies toward greater false positives.

The National Mammography Database: Preliminary Data.

The purposes of our study were to analyze screening mammography data submitted to the National Mammography Database (NMD) since its inception to confirm data collection feasibility, to draw parallels to data from the Breast Cancer Surveillance Consortium (BCSC), and to examine trends over time. We also retrospectively evaluated practice-level variation in terms of practice type, practice setting, census region, and annual volume. Data from 90 mammography facilities in the NMD registry were analyzed. The registry receives mammography data collected as part of standard clinical practice, including self-reported demographic information, clinical findings, screening mammography interpretation, and biopsy results. Outcome metrics calculated were cancer detection rate, recall rate, and positive predictive values for biopsy recommended (PPV2) and biopsy performed (PPV3). The NMD successfully collected and analyzed data for 3,181,437 screening mammograms performed between January 2008 and December 2012. Mean values for outcomes were cancer detection rate of 3.43 per 1000 (95% CI, 3.2-3.7), recall rate of 10% (95% CI, 9.3-10.7%), PPV2 of 18.5% (95% CI, 16.7-20.2%), and PPV3 of 29.2% (95% CI, 26.2-32.3%). No statistically significant difference was seen in performance measurements on the basis of practice type, practice setting, census region, or annual volume. NMD performance measurements parallel those reported by the BCSC. The NMD has become the fastest growing mammography registry in the United States, providing nationwide performance metrics and permitting comparison with published benchmarks. Our study shows the feasibility of using the NMD to audit mammography facilities and to provide current, ongoing benchmark data.

Radiographers supporting radiologists in the interpretation of screening mammography: a viable strategy to meet the shortage in the number of radiologists.

BackgroundAn alternative approach to the traditional model of radiologists interpreting screening mammography is necessary due to the shortage of radiologists to interpret screening mammograms in many countries.MethodsWe evaluated the performance of 15 Mexican radiographers, also known as radiologic technologists, in the interpretation of screening mammography after a 6 months training period in a screening setting. Fifteen radiographers received 6 months standardized training with radiologists in the interpretation of screening mammography using the Breast Imaging Reporting and Data System (BI-RADS) system. A challenging test set of 110 cases developed by the Breast Cancer Surveillance Consortium was used to evaluate their performance. We estimated sensitivity, specificity, false positive rates, likelihood ratio of a positive test (LR+) and the area under the subject-specific Receiver Operating Characteristic (ROC) curve (AUC) for diagnostic accuracy. A mathematical model simulating the consequences in costs and performance of two hypothetical scenarios compared to the status quo in which a radiologist reads all screening mammograms was also performed.ResultsRadiographer’s sensitivity was comparable to the sensitivity scores achieved by U.S. radiologists who took the test but their false-positive rate was higher. Median sensitivity was 73.3 % (Interquartile range, IQR: 46.7–86.7 %) and the median false positive rate was 49.5 % (IQR: 34.7–57.9 %). The median LR+ was 1.4 (IQR: 1.3-1.7 %) and the median AUC was 0.6 (IQR: 0.6–0.7). A scenario in which a radiographer reads all mammograms first, and a radiologist reads only those that were difficult for the radiographer, was more cost-effective than a scenario in which either the radiographer or radiologist reads all mammograms.ConclusionsGiven the comparable sensitivity achieved by Mexican radiographers and U.S. radiologists on a test set, screening mammography interpretation by radiographers appears to be a possible adjunct to radiologists in countries with shortages of radiologists. Further studies are required to assess the effectiveness of different training programs in order to obtain acceptable screening accuracy, as well as the best approaches for the use of non-physician readers to interpret screening mammography.

Identification of abnormal screening mammogram interpretation using Medicare claims data.

To develop and validate Medicare claims-based approaches for identifying abnormal screening mammography interpretation. Mammography data and linked Medicare claims for 387,709 mammograms performed from 1999 to 2005 within the Breast Cancer Surveillance Consortium (BCSC). Split-sample validation of algorithms based on claims for breast imaging or biopsy following screening mammography. Medicare claims and BCSC mammography data were pooled at a central Statistical Coordinating Center. Presence of claims for subsequent imaging or biopsy had sensitivity of 74.9 percent (95 percent confidence interval [CI], 74.1-75.6) and specificity of 99.4 percent (95 percent CI, 99.4-99.5). A classification and regression tree improved sensitivity to 82.5 percent (95 percent CI, 81.9-83.2) but decreased specificity (96.6 percent, 95 percent CI, 96.6-96.8). Medicare claims may be a feasible data source for research or quality improvement efforts addressing high rates of abnormal screening mammography.

Educational interventions to improve screening mammography interpretation: a randomized controlled trial.

The objective of our study was to conduct a randomized controlled trial of educational interventions that were created to improve performance of screening mammography interpretation. We randomly assigned physicians who interpret mammography to one of three groups: self-paced DVD, live expert-led educational seminar, or control. The DVD and seminar interventions used mammography cases of varying difficulty and provided associated teaching points. Interpretive performance was compared using a pretest-posttest design. Sensitivity, specificity, and positive predictive value (PPV) were calculated relative to two outcomes: cancer status and consensus of three experts about recall. The performance measures for each group were compared using logistic regression adjusting for pretest performance. One hundred two radiologists completed all aspects of the trial. After adjustment for preintervention performance, the odds of improved sensitivity for correctly identifying a lesion relative to expert recall were 1.34 times higher for DVD participants than for control subjects (95% CI, 1.00-1.81; p = 0.050). The odds of an improved PPV for correctly identifying a lesion relative to both expert recall (odds ratio [OR] = 1.94; 95% CI, 1.24-3.05; p = 0.004) and cancer status (OR = 1.81; 95% CI, 1.01-3.23; p = 0.045) were significantly improved for DVD participants compared with control subjects, with no significant change in specificity. For the seminar group, specificity was significantly lower than the control group (OR relative to expert recall = 0.80; 95% CI, 0.64-1.00; p = 0.048; OR relative to cancer status = 0.79; 95% CI, 0.65-0.95; p = 0.015). In this randomized controlled trial, the DVD educational intervention resulted in a significant improvement in screening mammography interpretive performance on a test set, which could translate into improved interpretative performance in clinical practice.

Variations in screening outcome among pairs of screening radiologists at non-blinded double reading of screening mammograms: a population-based study

Substantial inter-observer variability in screening mammography interpretation has been reported at single reading. However, screening results of pairs of screening radiologists have not yet been published. We determined variations in screening performances among pairs of screening radiologists at non-blinded double reading. We included pairs of screening radiologists with at least 7,500 screening examinations per pair, obtained between 1997 and 2011. During 2-year follow-up, breast imaging reports, surgical reports and pathology results were collected of all referred women and interval cancers. Referral rate, cancer detection rate, positive predictive value and sensitivity were calculated for each pair. A total of 310,906 screening mammograms, read by 26 pairs of screening radiologists, were included for analysis. The referral rate ranged from 1.0 % (95 % CI 0.8 %-1.2 %) to 1.5 % (95 % CI 1.3 %-1.8 %), the cancer detection rate from 4.0 (95 % CI 2.8-5.2) to 6.3 (95 % CI 4.5-8.0) per 1,000 screens. The programme sensitivity and positive predictive value of referral ranged from 55.1 % (95 % CI 45.1 %-65.1 %) to 81.5 % (95 % CI 73.4 %-89.6 %) and from 28.7 % (95 % CI 20.8 %-36.6 %) to 49.5 % (95 % CI 39.7 %-59.3 %), respectively. We found significant variations in screening outcomes among pairs of screening radiologists at non-blinded double reading. This stresses the importance of monitoring screening results on a local scale. • Substantial inter-observer variability in screening mammography interpretation is known at single reading • Population-based study showed significant variations in outcomes among pairs of screening radiologists • Local monitoring and regular feedback are important to optimise screening outcome.

Educational Interventions to Improve Screening Mammography Interpretation: A Randomized Controlled Trial

Educational Interventions to Improve Screening Mammography Interpretation: A Randomized Controlled Trial

Feasibility and Acceptability of Conducting a Randomized Clinical Trial Designed to Improve Interpretation of Screening Mammography

To 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. We 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. Of 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 10years 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 theirclinical 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. Community 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.

Radiology Residents' Performance in Screening Mammography Interpretation

Radiology Residents' Performance in Screening Mammography Interpretation