A deep-learning tool assessed 27,172 mammograms that radiologists had read as normal, then ranked women by their five-year breast cancer risk. The analysis included 2,011 cancers diagnosed later and more than five years of follow-up. Preliminary results suggest the AI risk score could help identify women who may need a different screening approach.
Called Transpara Risk Index, the system analyses digital mammograms and tomosynthesis scans. It estimates the statistical probability of a cancer diagnosis over the next five years by measuring breast density and tissue-texture patterns that may be difficult to spot by eye.
The model was trained on more than 200,000 mammograms and about 6,000 cancer cases, with more than 13 years of follow-up in its training data.
In the current study, the tool’s AUC was 0.71 at two years, 0.70 at three years and 0.69 at five years. AUC measures how well a system distinguishes between women who later develop cancer and those who do not. The highest-risk 10% accounted for about 30% of cancers diagnosed within five years. By comparison, the 10% with the greatest breast density accounted for 13%.
The findings remain preliminary. Six centres in the United States and Europe are testing the system independently at scale, using equipment from three manufacturers. The results do not establish a new screening schedule.
Marina Álvarez said the tool was trained on more than 200,000 mammograms and 6,000 cancer cases, with follow-up exceeding 13 years. An earlier external validation involving more than 65,000 studies from European and US centres found AUC values between 0.73 and 0.75. In that analysis, the 10% ranked at highest risk accounted for 39.3% of cancers diagnosed later.
Álvarez is a breast radiologist with the Sociedad Española de Radiología Médica (SERAM). She heads Radiodiagnóstico at Hospital Universitario Reina Sofía de Córdoba and researches at Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC). The two Córdoba institutions are the only Spanish centres participating in the current study.
A routine scan could make personalised screening easier to put into practice. Earlier approaches based on breast density faced barriers such as cost and time, as well as limits to precision. Clinical tests and genetic studies have faced similar barriers.
A separate European effort is testing AI in healthcare through a cardiovascular AI project covering six applications across 13 countries.
A Swedish study examined 20,187 mammograms from 7,430 women screened between 2008 and 2016. It recorded 741 cancers within two years, including 217 interval cancers, diagnosed after a negative screening result but before the next scheduled round. In that dataset, Transpara Risk had an AUC of 0.81 for short-term prediction. At 90% specificity, it identified 49.8% of interval cancers.
Depending on the estimated risk, doctors could consider changing mammogram intervals or adding more sensitive imaging, such as MRI. Contrast mammography and additional preventive measures could also be considered for women at higher risk.
