⚡ Quick Summary
A recent study evaluated the effectiveness of a commercial artificial intelligence (AI) algorithm in detecting subclinical breast cancer among women aged 50 to 69. The findings indicate that the AI algorithm can significantly predict the risk of future breast cancer, potentially leading to earlier diagnoses and improved patient outcomes.
🔍 Key Details
- 📊 Dataset: 116,495 women aged 50 to 69 with no prior breast cancer history
- 🧩 Technology: INSIGHT MMG AI algorithm (version 1.1.7.2; Lunit Inc)
- ⚙️ Study Design: Retrospective cohort study with data from multiple mammography screenings
- 📅 Study Period: Screening data from September 2004 to December 2018
🔑 Key Takeaways
- 📈 AI scores were higher for breasts that developed cancer compared to those that did not.
- 🔍 Mean absolute differences in AI scores increased significantly over the three screening rounds.
- 🏆 Areas under the curve (AUC) for detecting screening-detected cancer improved from 0.63 to 0.96 across rounds.
- 💡 The study suggests that AI can identify women at high risk of future breast cancer.
- 🌍 Personalized screening approaches could be developed based on AI risk assessments.
📚 Background
Early detection of breast cancer is crucial for reducing morbidity and mortality rates. Traditional screening methods, while effective, may not always identify subclinical cases. The integration of artificial intelligence into breast cancer screening represents a promising advancement, potentially enhancing the accuracy of risk assessments and enabling more personalized approaches to patient care.
🗒️ Study
This study was conducted using data from 116,495 women who underwent at least three consecutive biennial mammography screenings. The researchers utilized the INSIGHT MMG AI algorithm to analyze the continuous cancer detection scores, which ranged from 0 to 100, indicating the likelihood of cancer presence. The aim was to determine whether these scores could effectively predict the development of future breast cancer.
📈 Results
The results revealed that the mean absolute differences in AI scores among breasts developing screening-detected cancer were significantly higher than those that did not develop cancer. Specifically, the mean differences increased from 21.3 at the first round to 79.0 at the third round. The AUC for detecting screening-detected cancer reached an impressive 0.96 in the third round, indicating excellent predictive capability.
🌍 Impact and Implications
The implications of this study are profound. By leveraging AI algorithms for breast cancer detection, healthcare providers can potentially identify women at high risk much earlier than traditional methods allow. This could lead to personalized screening strategies that not only improve early detection rates but also enhance overall patient outcomes. The integration of AI in clinical practice could transform the landscape of breast cancer screening and management.
🔮 Conclusion
This study highlights the transformative potential of artificial intelligence in breast cancer detection. The ability of AI algorithms to predict future cancer development offers a promising avenue for enhancing screening protocols and improving patient care. Continued research and development in this field are essential to fully realize the benefits of AI in oncology.
💬 Your comments
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Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection.
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.
Author: [‘Gjesvik J’, ‘Moshina N’, ‘Lee CI’, ‘Miglioretti DL’, ‘Hofvind S’]
Journal: JAMA Netw Open
Citation: Gjesvik J, et al. Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection. Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection. 2024; 7:e2437402. doi: 10.1001/jamanetworkopen.2024.37402