In yet another demonstration of AI’s potential to improve mammography screening, a new study in Radiology shows that Lunit’s Insight MMG algorithm detected nearly a quarter of interval cancers missed by radiologists on regular breast screening exams.
Breast screening is one of healthcare’s most challenging cancer screening exams, and for decades has been under attack by skeptics who question its life-saving benefit relative to “harms” like false-positive biopsies.
- But AI has the potential to change the cost-benefit equation by detecting a higher percentage of early-stage cancers and improving breast cancer survival rates.
Indeed, 2024 has been a watershed year for mammography AI.
- Landmark studies like MASAI and the BreastScreen Norway study have shown that AI can reduce workloads while detecting more cancers, and the new study continues in that vein.
U.K. researchers used Insight MMG (also used in the BreastScreen Norway trial) to analyze 2.1k screening mammograms, of which 25% were interval cancers (cancers occurring between screening rounds) and the rest normal.
- The AI algorithm generates risk scores from 0-100, with higher scores indicating likelihood of malignancy, and this study was set at a 96% specificity threshold, equivalent to the average 4% recall rate in the U.K. national breast screening program.
In analyzing the results, researchers found …
- AI flagged 24% of the interval cancers and correctly localized 77%.
- AI localized a higher proportion of node-positive than node-negative cancers (24% vs. 16%).
- Invasive tumors had higher median risk scores than noninvasive (62 vs. 33), with median scores of 26 for normal mammograms.
Researchers also tested AI at a lower specificity threshold of 90%.
- AI detected more interval cancers at this level, but in real-world practice this would bump up recall rates.
It’s also worth noting that Insight MMG is designed for the analysis of 2D digital mammography, which is more common in Europe than DBT.
- For the U.S., Lunit is emphasizing its recently cleared Insight DBT algorithm, which may perform differently.
The Takeaway
As with the MASAI and BreastScreen Norway results, the new study points to an exciting role for AI in making mammography screening more accurate with less drain on radiologist resources. But as with those studies, the new results must be interpreted against Europe’s double-reading paradigm, which differs from the single-reading protocol used in the U.S.
