Good news!
"Researchers have published the full results of the largest randomized trial of AI cancer screening to date, comparing mammograms read by one radiologist assisted by AI with the standard two-radiologist review.
The AI-supported process cut the radiologists’ workload by 44 percent and detected 29 percent more cancers, without additional false positives. Women who received a negative result during their AI-assisted mammogram ended up having 12 percent fewer cancer diagnoses before their next scheduled screening than those in the control group, suggesting that the AI screen missed fewer aggressive cancers."
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- First full results of a randomised trial investigating the use of AI in a national breast cancer screening programme finds AI-supported mammography screening is more effective across many measures than standard mammography.
- AI-supported breast cancer screening identified more women with clinically relevant cancers during the screening without a higher rate of false positives.
- Additionally, women who underwent AI-supported screening were less likely to be diagnosed with more aggressive and advanced breast cancer in the two years following.
- Authors say these findings could justify implementing AI in mammography screening programmes, particularly in the context of health professional workforce shortages.
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From the abstract:
"Summary
Background
Evidence indicates that artificial intelligence (AI) can improve mammography screening by increasing cancer detection and reducing screen reading workload, but its effect on interval cancers (primary breast cancers diagnosed between two screening rounds or within 2 years after the last scheduled screening that were not detected at screening) is unknown. We aimed to compare the interval cancer rate in AI-supported mammography screening with standard double reading without AI.
Methods
In this Swedish randomised, controlled, non-inferiority, single-blinded, population-based screening accuracy trial, participants were allocated in a 1:1 ratio to either AI-supported mammography screening (the intervention group) or standard double reading without AI (the control group).
AI was used to triage examinations to single or double reading by radiologists and for detection support. This is a protocol-defined analysis of the primary outcome, interval cancer rate, with a 20% non-inferiority margin. Secondary outcomes reported in this analysis are interval cancer characteristics, sensitivity, specificity, and sensitivity by age, breast density, and cancer type (in-situ and invasive). ...
Findings
Between April 12, 2021, and Dec 7, 2022, 105 934 women were randomly assigned to the intervention or control group, of whom 19 were excluded from the analysis. Median age was 53·8 years (IQR 46·5–63·3) in the intervention group and 53·7 years (46·5–63·2) in the control group. Interval cancer rates were 1·55 (95% CI 1·23–1·92) and 1·76 (1·42–2·15) per 1000 participants in the intervention and control group respectively, a non-inferior proportion ratio of 0·88 (95% CI 0·65–1·18; p=0·41). Descriptively, the intervention group had fewer interval cancers that were invasive (75 vs 89), T2+ (38 vs 48), or non-luminal A (43 vs 59) than the control group. Sensitivity was higher in the intervention group (80·5% [95% CI 76·4–84·2]) than the control group (73·8% [68·9–78·3]; p=0·031), an effect consistent across age and breast density, and for invasive cancer but not for in-situ cancer. Specificity was 98·5% (95% CI 98·4–98·6) for both groups (p=0·88).
Interpretation
AI-supported mammography screening showed consistently favourable outcomes compared with standard double reading, with a non-inferior interval cancer rate, fewer interval cancers with unfavourable characteristics, higher sensitivity, and the same specificity, while also reducing screen reading workload. These findings imply that AI-supported mammography screening can efficiently improve screening performance compared with standard double reading and may be considered for implementation in clinical practice."
AI support in breast cancer screening: Fewer missed cancer cases (original news release) "There were fewer cases of breast cancer between two screening rounds, and of the cancers that did develop, fewer were advanced or aggressive. The final results from Lund University's MASAI trial are now available, and they show further benefits of AI-supported breast cancer screening. The study has already shown that AI support in mammography screening contributes to a 29 percent increase in detected breast cancers compared to traditional screening."
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