Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden and facilitating early detection. An AI algorithm was developed on 53,858 breasts from 12,694 patients who underwent screening or diagnostic MRI and accrued over 12 years, with 2,331 confirmed cancers. A first U-Net was trained to segment lesions and identify regions of concern. A second convolutional network was trained to detect malignant cancer using features extracted by the U-Net. This network was then fine-tuned to estimate the risk of developing cancer within a year in cases that radiologists considered normal or likely benign. Risk predictions from this AI were evaluated with a retrospective analysis of 9,183 breasts from a high-risk screening cohort, which were not used for training. Statistical analysis focused on the tradeoff between number of omitted exams versus negative predictive value, and number of potential early detections versus positive predictive value. The AI algorithm identified regions of concern that coincided with future tumors in 52% of screen-detected cancers. Upon directed review, a radiologist found that 71.3% of cancers had a visible correlate on the MRI prior to diagnosis, 65% of these correlates were identified by the AI model. Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist. Additionally, screening burden could have been reduced in 16% of lower-risk cases by recommending a later follow-up without compromising current interval cancer rate. With increasing datasets and improving image quality we expect this new AI-aided, adaptive screening to meaningfully reduce screening burden and improve early detection.

翻译：具有终生乳腺癌高风险的女性需每年接受补充性MRI筛查。我们提出基于当前MRI预测一年内罹患乳腺癌的风险，旨在减少筛查负担并促进早期检测。该人工智能算法基于12,694名患者的53,858例乳房图像开发，这些患者在过去12年间接受筛查或诊断性MRI，其中共确诊2,331例癌症。首先训练U-Net网络分割病灶并识别可疑区域，随后训练第二个卷积网络，利用U-Net提取的特征检测恶性肿瘤。该网络进一步微调，用于评估放射科医生判定为正常或可能良性的病例在一年内罹患癌症的风险。我们通过一项包含9,183例高危筛查队列乳房的回顾性分析（该队列未用于训练）评估该AI的风险预测能力。统计分析聚焦于省略检查次数与阴性预测值之间的权衡，以及潜在早期检测数量与阳性预测值之间的权衡。AI算法识别出的可疑区域中，52%的筛查检出癌症与未来肿瘤位置重合。经定向复查，放射科医生发现71.3%的癌症在诊断前的MRI上存在可见相关性，其中65%的相关性被AI模型识别。若对所有病例中AI预测风险最高的10%重新评估这些区域，放射科医生可能实现最多33%的早期检测。此外，对16%的低风险病例推荐延迟随访可减少筛查负担，且不影响当前间期癌发生率。随着数据集的扩大和影像质量的提升，我们期待这种新型AI辅助自适应筛查能显著降低筛查负担并改善早期检测效果。