Radiologists' interpretive skills in screening vs. diagnostic mammography: are they related?

Elmore, Joann G.; Cook, Andrea J.; Bogart, Andy; Carney, Patricia A.; Geller, Berta M.; Taplin, Stephen H.; Buist, Diana S.M.; Onega, Tracy; Lee, Christoph I.; Miglioretti, Diana L.

doi:10.1016/j.clinimag.2016.06.014

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…As Gunderman notes, expertise in one radiologic domain does not necessarily apply to every domain (Gunderman et al, 2001). For instance, Beam et al (2006) and Elmore et al (2016) note that mammographers have two distinct tasks: (1) Interpretation of screening mammograms and (2) Interpretation of abnormal screening mammographic findings. The first task requires evaluation of standard images from a large population of individuals without specific signs or symptoms (considered ‘perceptual’), whereas the second task requires careful analysis of specific abnormalities (considered more ‘cognitive’) (Beam et al, 2006; Elmore et al, 2016).…”

Section: Challenges To the Functional Definition Of Expertisementioning

confidence: 99%

“…Both studies found only a moderate correlation between radiologists’ performance in the two domains. In other words, proficiency in one area did not guarantee proficiency in the other (Beam et al, 2006; Elmore et al, 2016). The presence of wide disparities in screening and diagnostic interpretive skills in the same individual has been described as ‘expertise disequilibrium’ (Beam et al, 2006).…”

Section: Challenges To the Functional Definition Of Expertisementioning

confidence: 99%

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Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Waite

Grigorian

Alexander

et al. 2019

Front. Hum. Neurosci.

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Radiologists rely principally on visual inspection to detect, describe, and classify findings in medical images. As most interpretive errors in radiology are perceptual in nature, understanding the path to radiologic expertise during image analysis is essential to educate future generations of radiologists. We review the perceptual tasks and challenges in radiologic diagnosis, discuss models of radiologic image perception, consider the application of perceptual learning methods in medical training, and suggest a new approach to understanding perceptional expertise. Specific principled enhancements to educational practices in radiology promise to deepen perceptual expertise among radiologists with the goal of improving training and reducing medical error.

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Section: Challenges To the Functional Definition Of Expertisementioning

confidence: 99%

Section: Challenges To the Functional Definition Of Expertisementioning

confidence: 99%

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Waite

Grigorian

Alexander

et al. 2019

Front. Hum. Neurosci.

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“…Reports on variability in interpretation of screening (17–19) and diagnostic (20,21) mammography investigated the impact of physician characteristics such as subspecialty training, years of experience, and volumes of yearly interpreted examinations on improved performance (22–24). In our analysis of facility characteristics, community practices were more likely to utilize subdivisions in their category 4 examinations ( P < .001).…”

Section: Discussionmentioning

confidence: 99%

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database

Elezaby

Bhargavan-Chatfield

et al. 2018

Radiology

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Purpose To determine the utilization and positive predictive value (PPV) of the American College of Radiology (ACR) Breast Imaging Data and Reporting System (BI-RADS) category 4 subdivisions in diagnostic mammography in the National Mammography Database (NMD). Materials and Methods This study involved retrospective review of diagnostic mammography data submitted to the NMD from January 1, 2008 to December 30, 2014. Utilization rates of BI-RADS category 4 subdivisions were compared by year, facility (type, location, census region), and examination (indication, finding type) characteristics. PPV3 (positive predictive value for biopsies performed) was calculated overall and according to category 4 subdivision. The χ test was used to test for significant associations. Results Of 1 309 950 diagnostic mammograms, 125 447 (9.6%) were category 4, of which 33.3% (41 841 of 125 447) were subdivided. Subdivision utilization rates were higher (P < .001) in practices that were community, suburban, or in the West; for examination indication of prior history of breast cancer; and for the imaging finding of architectural distortion. Of 41 841 category 4 subdivided examinations, 4A constituted 55.6% (23 258 of 41 841) of the examinations; 4B, 31.8% (13 302 of 41 841) of the examinations; and 4C, 12.6% (5281 of 41 841) of the examinations. Pathologic outcomes were available in 91 563 examinations, and overall category 4 PPV3 was 21.1% (19 285 of 91 563). There was a statistically significant difference in PPV3 according to category 4 subdivision (P < .001): The PPV of 4A was 7.6% (1274 of 16 784), that of 4B was 22% (2317 of 10 408), and that of 4C was 69.3% (2839 of 4099). Conclusion Although BI-RADS suggests their use, subdivisions were utilized in the minority (33.3% [41 841 of 125 447]) of category 4 diagnostic mammograms, with variability based on facility and examination characteristics. When subdivisions were used, PPV3s were in BI-RADS-specified malignancy ranges. This analysis supports the use of subdivisions in broad practice and, given benefits for patient care, should motivate increased utilization. RSNA, 2018 Online supplemental material is available for this article.

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“…In the case of radiologists, previous knowledge and expertise (including their a priori expectations about a task) can change the features they may use to accomplish their task goals. Further, the interpretation of medical imaging is highly task-dependent: radiologic expertise in one domain does not necessarily rely on the same skills as expertise in another domain ( Beam, Conant, & Sickles, 2006 ; Elmore et al, 2016 ; Gunderman et al, 2001 ). Task demands can similarly change within a specific domain and therefore affect search behavior: for example, in patients with known renal cell cancer, radiologists may prolong their search of the lungs, looking carefully for nodules that might represent metastatic lesions.…”

Section: Introductionmentioning

confidence: 99%

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

et al. 2020

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Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health. Error rates in radiology The long-term goal of most studies of radiologist performance is to reduce error. Although it is difficult to determine the precise error rates in current clinical practice, it is clear that reductions in error rate would improve patient care. The diagnostic error rate in a typical clinical practice (comprising both normal

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Radiologists' interpretive skills in screening vs. diagnostic mammography: are they related?

Cited by 5 publications

References 26 publications

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

ACR BI-RADS Assessment Category 4 Subdivisions in Diagnostic Mammography: Utilization and Outcomes in the National Mammography Database

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

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