UCLA Study Shows How Transpara’s Breast AI Helps Detect More Cancers in Real-World Screening Populations
A recent study from UCLA, published in the Journal of Breast Imaging, underscores the potential of Transpara’s advanced Breast AI technology in improving cancer detection by reducing false negative results in diverse U.S. screening populations. The research, titled “External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers,” showed that Transpara accurately identified nearly 50% of cancers previously missed, particularly in patients with dense breast tissue.
Key Findings of the Study
The UCLA study demonstrated that Transpara detected false negative breast cancers, with the majority being invasive and categorized as luminal A subtype. This subtype is the most common, representing 50-60% of all breast cancers. The study specifically highlighted that, in the Digital Breast Tomosynthesis (DBT) cohort, all of the interval cancers detected were in women with dense breast tissue. Dense breasts are known to reduce the effectiveness of traditional mammography, often making it more difficult to detect cancers. Furthermore, women with dense breast tissue face a higher individual risk for breast cancer, making early detection crucial for improving patient outcomes.
The ability of Transpara to accurately identify these missed cancers, especially in women with dense breast tissue, could have a significant impact on early diagnosis and the treatment of breast cancer. Given that dense breast tissue often leads to decreased mammographic sensitivity, the study suggests that Transpara’s AI could be an invaluable tool for enhancing the overall quality of breast cancer screening.
Transpara’s Impact on Detection
The study was designed to evaluate how artificial intelligence could assist in identifying cancers that were missed by radiologists during initial screenings. According to the Breast Cancer Screening Consortium, the rate of false negatives in U.S. breast cancer screenings is approximately 0.8 per 1,000 examinations. By utilizing Transpara’s AI, however, the detection of false negative cancers improved significantly. This result is particularly encouraging for the many women with dense breasts, where the risk of undetected cancer is higher.
“While the false negative rate in breast cancer screening is relatively low, minimizing this rate even further is essential to maximizing the benefits of screening,” explained Dr. Alejandro Rodriguez Ruiz, VP of Innovation and Clinical Strategy at ScreenPoint. “These findings are particularly noteworthy because they were derived from real-world screening populations, not datasets that were specifically enriched with cancer cases. This makes the results more relevant and transferable to everyday clinical practice.”
Proven Track Record of Transpara’s AI
Transpara has been validated in over 35 peer-reviewed studies, making it the only AI algorithm to undergo extensive evaluation in large-scale, real-world screening populations across multiple countries. These include the U.S. (UCLA), the Netherlands, the UK, Denmark, Norway, and Spain. The AI technology assists radiologists by providing a ‘second pair’ of eyes when reading mammography exams, both DBT and FFDM. This helps detect cancers earlier, reduce recall rates, and ultimately optimize workflow for radiologists.
Research shows that up to 45% of interval cancers, which are cancers that appear between screenings, can be detected earlier using Transpara’s AI technology. This can lead to better treatment outcomes and a more efficient diagnostic process for both patients and healthcare providers.
Conclusion
Transpara’s advanced AI technology has the potential to revolutionize breast cancer detection, particularly in diverse and real-world screening environments. By significantly reducing false negative rates, Transpara can help ensure that more women are diagnosed with breast cancer at earlier, more treatable stages, resulting in better health outcomes and a more effective use of radiologists’ time.