A Norwegian retrospective study evaluated Lunit INSIGHT MMG, an AI tool by Lunit for predicting future breast cancer risk in women through mammography. The study analyzed data from 116,495 women aged 50-69 years across nine breast centers in Norway (2004-2018) and used the difference in AI scores between each woman’s two breasts to predict cancer risk.
Cancerous cases showed higher score differences, with mean scores in cancerous breasts reaching 21.3, 30.7, and 79.0 by the third screening round, compared to stable scores of 9.9, 9.6, and 9.3 in non-cancerous breasts. Over three biennial screenings, these AI score differences became more predictive, with AUCs increasing from 0.63 in the first round to 0.96 by the third for screening-detected cancers, and from 0.64 to 0.77 for interval cancers. A 20-point score difference between breasts was identified as a practical threshold for elevated risk, suggesting that women with such discrepancies could benefit from more frequent screening.
Take-Home Message: AI in mammography could transform breast cancer screening by offering early, risk-based predictions. Lunit INSIGHT MMG not only detects present cancers but also identifies women at elevated future risk, enabling timely interventions, potentially reduced mortality, and personalized preventive strategies.
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Artificial intelligence algorithm for subclinical breast cancer detection
JAMA Network Open, 2024
Abstract
Importance
Early breast cancer detection is associated with lower morbidity and mortality.
Objective
To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer.
Design, Setting, and Participants
This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2023) for breast cancer detection and screening data from multiple, consecutive rounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from September 2023 to August 2024.
Exposure
Artificial intelligence algorithm score indicating suspicion for the presence of breast cancer. The algorithm provided a continuous cancer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mammogram.
Main Outcomes and Measures
Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative.
Results
The mean (SD) age at the first study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breast cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) at the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, and 34.0 (33.6) at the third study round. The mean (SD) differences among women who did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (17.4) at the second study round, and 9.3 (17.3) at the third study round. Areas under the receiver operating characteristic curve for the absolute difference were 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0.96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the third study round for interval cancers.
Conclusions and Relevance
In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breasts developing vs not developing cancer 4 to 6 years before their eventual detection. These findings suggest that commercial AI algorithms developed for breast cancer detection may identify women at high risk of a future breast cancer, offering a pathway for personalized screening approaches that can lead to earlier cancer diagnosis.