Enhancing Breast Radiographer Performance in Tomosynthesis Screening via Automated Software

Overview

This study evaluated the impact of automated software assessment on breast radiographers' performance in positioning and compression during digital breast tomosynthesis (DBT) screening. Use of the Volpara Analytics tool enabled systematic quality assurance, leading to improved rates of high-quality mammograms across radiographers with varying experience levels.

Background

Breast cancer screening relies heavily on mammography accuracy, which is influenced by proper breast positioning and compression. Digital breast tomosynthesis (DBT) offers improved cancer detection but still depends on these factors. Traditional visual assessment methods for positioning quality are time-consuming and subject to inter-observer variability. Automated software tools have emerged to provide objective, systematic evaluation of positioning and compression, potentially enhancing radiographer performance and screening quality.

Data Highlights

The study retrospectively analyzed DBT images from 10,269 women aged 45 enrolled in the RIBBS trial, using Volpara Analytics software to assess breast positioning and compression. Positioning was categorized into four classes (perfect, good, moderate, inadequate) across multiple metrics for CC and MLO views. Compression pressure was calculated and classified as low, target (7-15 kPa), or high. Radiographers with experience ranging from 0 to over 25 years participated, allowing personalized feedback and learning.

Key Findings

• Automated software assessment enabled systematic, quantitative evaluation of breast positioning and compression during DBT screening.
• Positioning metrics based on international standards (PGMI) were applied, categorizing images into perfect, good, moderate, and inadequate classes.
• The percentage of images rated as perfect or good (% P + G) served as a comprehensive measure of positioning accuracy.
• Compression pressure was objectively measured, with a target range of 7-15 kPa to optimize image quality and patient comfort.
• Use of the software facilitated personalized feedback for radiographers, improving the quality of mammograms regardless of their prior experience level.

Clinical Implications

Incorporating automated software tools like Volpara Analytics into DBT screening workflows can enhance quality assurance by providing objective, reproducible assessments of breast positioning and compression. This supports radiographers in achieving higher quality images, potentially improving cancer detection rates and patient experience. Personalized feedback based on software metrics can help standardize performance across radiographers with diverse experience.

Conclusion

Automated software assessment represents a valuable tool to improve breast radiographer performance in tomosynthesis screening by enabling systematic quality evaluation and personalized learning. This approach may contribute to enhanced screening accuracy and better clinical outcomes.

References

1. RIBBS Study Protocol and ClinicalTrials.gov NCT05675085