标题： 乳腺终末导管小叶单位退化深度学习评估：向乳腺癌风险自动化预测迈进 显示英文标题

标题： Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk

摘要： 终端导管小叶单位（TDLU）退化是乳腺中产奶结构的退化。 较少的TDLU退化的女性更容易患上乳腺癌。 在大规模队列研究中，研究TDLU退化的主要瓶颈是需要耗费大量人力的手动评估TDLU。 我们开发了一种计算病理学解决方案，以自动捕捉TDLU退化测量值。 从护士健康研究（NHS）中获得了良性乳腺活检的全切片图像（WSIs）。 第一组92张WSIs被标注了TDLU、腺泡和脂肪组织，以训练深度卷积神经网络（CNN）模型来检测腺泡，并分割TDLU和脂肪组织。 这些网络被整合到一个计算方法中，以捕捉包括每组织面积的TDLU数量、中位TDLU跨度和每TDLU的中位腺泡数在内的TDLU退化测量值。 我们通过与手动获取的测量值进行比较，在40张额外的WSIs上验证了我们的方法。 我们的CNN模型检测腺泡的F1得分为0.73$\pm$0.09，分割TDLU和脂肪组织的Dice得分为0.86$\pm$0.11和0.86$\pm$0.04，分别。 在40张WSIs上对每组织面积的TDLU数量、中位TDLU跨度和每TDLU的中位腺泡数的手动评估的观察者间ICC得分分别为0.71，95% CI [0.51, 0.83]，0.81，95% CI [0.67, 0.90]，和0.73，95% CI [0.54, 0.85]。 在10/40张WSIs上评估了观察者内的可靠性，ICC得分大于0.8。 自动化结果与两名观察者的平均值之间的观察者间ICC得分分别为：每组织面积的TDLU数量为0.80，95% CI [0.63, 0.90]，中位TDLU跨度为0.57，95% CI [0.19, 0.77]，以及每TDLU的中位腺泡数为0.80，95% CI [0.62, 0.89]。 通过手动和自动化评估的TDLU退化测量值与年龄和绝经状态呈负相关。 显示英文摘要

摘要： Terminal ductal lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study (NHS). A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73$\pm$0.09, and segmented TDLUs and adipose tissue with Dice scores of 0.86$\pm$0.11 and 0.86$\pm$0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 95% CI [0.51, 0.83], 0.81, 95% CI [0.67, 0.90], and 0.73, 95% CI [0.54, 0.85], respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80, 95% CI [0.63, 0.90] for number of TDLUs per tissue area, 0.57, 95% CI [0.19, 0.77] for median TDLU span, and 0.80, 95% CI [0.62, 0.89] for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status.