Park Eun Kyung, Lee Hyeonsoo, Kim Minjeong, Kim Taesoo, Kim Junha, Kim Ki Hwan, Kooi Thijs, Chang Yoosoo, Ryu Seungho
Department of Radiology, We Comfortable Clinic, Seoul 07327, Republic of Korea.
Lunit Inc., Seoul 06241, Republic of Korea.
Diagnostics (Basel). 2024 Jun 7;14(12):1212. doi: 10.3390/diagnostics14121212.
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; < 0.001) and Gail model (AUC: 0.52; < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
本研究的目的是基于乳腺钼靶图像开发一种用于未来乳腺癌风险预测的人工智能（AI）模型，研究该AI模型的可行性，并将该AI模型、临床统计风险模型以及Mirai（一种基于乳腺钼靶筛查图像用于1至5年乳腺癌风险预测的先进深度学习算法）进行比较。我们使用来自21438名女性的总共36995次连续乳腺钼靶检查（富含癌症的钼靶图像，占17.5%）训练并开发了一个深度学习模型。为了确定AI预测模型的可行性，收集了钼靶图像和详细的临床信息。获得了1至5年结果的C指数和受试者操作特征曲线下面积（AUC）。我们使用德龙检验比较了我们的AI预测模型、Mirai以及临床统计风险模型（包括泰勒 - 库齐克（TC）模型和盖尔模型）的AUC。总共独立收集了16894张钼靶图像用于外部验证，其中4002例在5年内随后被诊断为癌症。我们的AI预测模型的C指数为0.76，预测1至5年风险的AUC分别为0.90、0.84、0.81、0.78和0.81。我们的AI预测模型的AUC显著高于TC模型（AUC：0.57；<0.001）和盖尔模型（AUC：0.52；<0.001），并且与Mirai表现相似。使用钼靶图像和人工智能驱动的影像生物标志物的深度学习AI模型在推进准确的乳腺癌风险预测方面具有巨大潜力。
