Transpara Improves Cancer Detection by Decreasing False Negative Cancers

ScreenPoint Medical's Transpara Accurately Identified Half of False Negative Cancers – all Invasive, and Often in Dense Breasts – in a Large Retrospective Study using Screening Population Dataset

NIJMEGEN, The Netherlands, Nov. 19, 2024 /PRNewswire/ -- Results of a UCLA study recently published in the Journal of Breast Imaging demonstrates that Transpara's proven Breast AI may improve cancer detection by decreasing false negative cancers when applied to a heterogeneous, real-world U.S. screening population. The study, "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 percent of false negative breast cancers, the majority in dense breasts.

The false negative cancers detected by Transpara were all invasive and predominantly (82%) luminal A subtype. The luminal A breast cancer is the most common subtype, representing 50–60% of all breast cancers. In the Digital Breast Tomosynthesis (DBT) cohort, all of the interval cancers detected were in dense breasts. As dense breast tissue is often associated both with decreased mammographic sensitivity and increased individual risk, the ability of Transpara to detect these interval cancers earlier could deliver better health outcomes. 

The study was designed to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone. According to the Breast Cancer Screening Consortium, the false negative rate in the United States is 0.8 per 1000 examinations.

"While the false negative rate in breast cancer screening is low, minimizing the false negative cancer rate is critical to achieving the greatest benefit of screening," said Alejandro Rodriguez Ruiz, PhD, VP of Innovation and Clinical Strategy at ScreenPoint. "These results are particularly strong as the study did not use cancer enriched datasets. As with the MASAI Trial, this study used actual screening populations, which makes the results more transferable to real-world clinical use."

With 35+ peer-reviewed studies, Transpara is the only AI algorithm evaluated in large-scale real-world screening populations multiple times (UCLA, Dutch Breast Cancer Screening Program, UK Breast Screening Program, Capital Region of Denmark, Lund University, Norwegian Cancer Registry, Reina Sofia Hospital Cordoba ). Transpara assists radiologists with the reading of mammography exams (both DBT & FFDM), providing a 'second pair' of eyes to help detect cancers earlier and reduce recall rates. Research shows that up to 45% of interval cancers can be found earlier using Transpara, while helping to reduce workload and optimize workflow.

About ScreenPoint Medical 

ScreenPoint Medical translates cutting edge machine learning research into technology accessible by radiologists to improve screening workflow, decision confidence and breast cancer risk assessment. Transpara is trusted by radiologists globally because it has been developed by experts in machine learning and image analysis and updated with user feedback from world-renowned breast imagers. See all the proof at: https://screenpoint-medical.com/published-evidence/peer-reviewed-publications 

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SOURCE ScreenPoint Medical