Lowry Kathryn P, Zuiderveld Case C
Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA; Fred Hutchinson Cancer Center, Seattle, WA, USA.
University of Washington School of Medicine, Seattle, WA, USA.
Radiol Clin North Am. 2024 Jul;62(4):619-625. doi: 10.1016/j.rcl.2024.02.004. Epub 2024 Mar 21.
Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.
基于常见临床风险因素的乳腺癌风险预测模型用于识别适合进行高风险筛查和预防的女性。不幸的是,这些模型的鉴别准确性有限,在代表性不足的种族和族裔群体中表现存在差异。随着基于乳腺X线摄影的人工智能乳腺癌风险模型的发展,人工智能(AI)和深度学习领域正在迅速推动乳腺癌风险预测领域的发展。早期研究表明,基于乳腺X线摄影的人工智能风险模型可能比传统的基于风险因素的模型表现更好,且性能更公平。