Messaggi chiave
- Vengono presentati i risultati di uno studio di coorte retrospettivo che si è proposto di valutare la capacità di un algoritmo commerciale di intelligenza artificiale (AI) di stimare il rischio di sviluppo di carcinoma mammario (BC). A tale scopo, l’algoritmo è stato testato sulle immagini mammografiche di 116.495 donne di età compresa tra 50 e 69 anni, senza alcuna anamnesi pregressa di BC, sottoposte ad almeno tre round biennali consecutivi di screening nel periodo settembre 2004 – dicembre 2018.
- 1265 donne hanno sviluppato un tumore screen-detected (SD) al terzo round di screening e 342 un tumore intervallo (IC) dopo tre round negativi. Le differenze assolute medie di punteggio AI nelle donne con tumore SD sono risultate pari a 21,3 al primo round, 30,7 al secondo e 79,0 al terzo. Per le donne con IC e le donne che non hanno sviluppato alcun tumore, le differenze medie sono state di 19,7 e 9,9, 21,0 e 9,6 e 34,0 e 9,3, rispettivamente, al primo, secondo e terzo round. Le aree sotto la curva ROC per la differenza assoluta sono state di 0,63, 0,72 e 0,96 nei tre round di screening per i tumori SD e di 0,64, 0,65 e 0,77 per gli IC.
- I risultati dello studio suggeriscono che algoritmi commerciali di AI concepiti per la diagnosi di carcinoma mammario sono in grado di identificare le donne ad alto rischio di sviluppo della malattia, offrendo l’opportunità di disegnare approcci di screening personalizzati che possano portare a una significativa anticipazione della diagnosi.
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.