Patient and Radiologist Characteristics Associated With Accuracy of Two Types of Diagnostic Mammograms

Abstract

Objective Earlier studies of diagnostic mammography found wide unexplained variability in accuracy among radiologists. We assessed patient and radiologist characteristics associated with the interpretive performance of two types of diagnostic mammography. Materials and Methods Radiologists interpreting mammograms in seven regions of the United States were invited to participate in a survey that collected information on their demographics, practice setting, breast imaging experience, and self-reported interpretive volume. Survey data from 244 radiologists were linked to data on 274,401 diagnostic mammograms performed for additional evaluation of a recent abnormal screening mammogram or to evaluate a breast problem, between 1998 and 2008. These data were also linked to patients' risk factors and follow-up data on breast cancer. We measured interpretive performance by false-positive rate, sensitivity, and AUC. Using logistic regression, we evaluated patient and radiologist characteristics associated with false-positive rate and sensitivity for each diagnostic mammogram type. Results Mammograms performed for additional evaluation of a recent mammogram had an overall false-positive rate of 11.9%, sensitivity of 90.2%, and AUC of 0.894; examinations done to evaluate a breast problem had an overall false-positive rate of 7.6%, sensitivity of 83.9%, and AUC of 0.871. Multiple patient characteristics were associated with measures of interpretive performance, and radiologist academic affiliation was associated with higher sensitivity for both indications for diagnostic mammograms. Conclusion These results indicate the potential for improved radiologist training, using evaluation of their own performance relative to best practices, and for improved clinical outcomes with health care system changes to maximize access to diagnostic mammography interpretation in academic settings.

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

“…In our analysis of facility characteristics, community practices were more likely to utilize subdivisions in their category 4 examinations ( P < .001). This may appear contradictory to prior reports that emphasized the role of subspecialization in adherence to BI-RADS; a characteristic commonly associated with academics (17,22,23,25). In a study by Miglioretti et al (20), association with an academic institution was the strongest predictor of improved diagnostic accuracy.…”

A machine learning model based on readers’ characteristics to predict their performances in reading screening mammograms

“…A more comprehensive reader characteristics can also be collected from readers and fed into the model to improve the accuracy of the model. For example, whether working fulltime or part-time [11] or being affiliated with academic centres [38] could affect the performance. The current study is a proof-of-concept study and although the proposed machine learning model outperformed the baseline model for categorising high-and low-performing readers, the added benefits of the variables describing additional case and reader characteristics can be explored in the future works.…”

How Many of the Biopsy Decisions Taken at Inexperienced Breast Radiology Units Were Correct?

“…Because of the fact that the evaluating specialist has long experience in BI-RADS for breast radiology, we believe in the accuracy of the categorical decisions made (34). As a matter of fact, the studies performed have shown that factors influencing correct decision-making in BI-RADS include the experience of radiologist, his/her interest in dense breast radiology and the high number of studies s/he evaluates on an annual basis (35)(36)(37).…”