Tan Hongna, Wu Qingxia, Wu Yaping, Zheng Bingjie, Wang Bo, Chen Yan, Du Lijuan, Zhou Jing, Fu Fangfang, Guo Huihui, Fu Cong, Ma Lun, Dong Pei, Xue Zhong, Shen Dinggang, Wang Meiyun
Department of Radiology, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, China.
Imaging Diagnosis of Neurological Diseases and Research Laboratory of Henan Province, Zhengzhou, China.
Insights Imaging. 2025 May 21;16(1):109. doi: 10.1186/s13244-025-01983-x.
We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography.
Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian women between August 2012 and December 2018 were enrolled. The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3-4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. We further evaluate AIS by conducting a counterbalance-designed AI-assisted study, where ten radiologists read 1302 cases with/without AIS assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score were measured.
The AIS yielded AUC values of 0.995, 0.933, and 0.947 for malignancy diagnosis in the validation set, testing set 1, and testing set 2, respectively. Within BI-RADS 3-4 subgroups with pathological results, AIS downgraded 83.1% of false-positives into benign groups, and upgraded 54.1% of false-negatives into malignant groups. AIS also successfully assisted radiologists in identifying 7 out of 43 malignancies initially diagnosed with BI-RADS 0, with a specificity of 96.7%. In the counterbalance-designed AI-assisted study, the average AUC across ten readers significantly improved with AIS assistance (p = 0.001).
AIS can accurately detect and diagnose breast cancer on mammography and further serve as a supportive tool for BI-RADS categorization.
An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists.
The false positive and negative rates of mammography diagnosis remain high. The AIS can yield a high AUC for malignancy diagnosis. The AIS is important in stratifying BI-RADS categorization.
我们开发了一种人工智能系统（AIS），该系统使用多视图多层次卷积神经网络，用于乳腺钼靶摄影中的乳腺癌检测、诊断以及BI-RADS分类支持。
纳入了2012年8月至2018年12月期间12433名亚洲女性的24866个乳房。该研究包括三个部分：（1）评估AIS在恶性肿瘤诊断中的性能；（2）对BI-RADS 3-4亚组进行AIS分层分析；（3）在AIS辅助下重新评估BI-RADS 0类乳房。我们通过进行一项平衡设计的人工智能辅助研究进一步评估AIS，其中十位放射科医生阅读了1302例有/无AIS辅助的病例。测量了受试者操作特征曲线下面积（AUC）、敏感性、特异性、准确性和F1分数。
在验证集、测试集1和测试集2中，AIS对恶性肿瘤诊断的AUC值分别为0.995、0.933和0.947。在有病理结果的BI-RADS 3-4亚组中，AIS将83.1%的假阳性降级为良性组，将54.1%的假阴性升级为恶性组。AIS还成功协助放射科医生在最初诊断为BI-RADS 0类的43例恶性肿瘤中识别出7例，特异性为96.7%。在平衡设计的人工智能辅助研究中，在AIS辅助下，十位读者的平均AUC显著提高（p = 0.001）。
AIS可以在乳腺钼靶摄影中准确检测和诊断乳腺癌，并进一步作为BI-RADS分类的支持工具。
开发并验证了一种采用深度学习算法的人工智能风险评估工具，以增强乳腺钼靶摄影中的乳腺癌诊断，提高风险分层准确性，特别是在乳房致密的患者中，并作为放射科医生的决策支持辅助工具。
乳腺钼靶摄影诊断的假阳性和假阴性率仍然很高。AIS在恶性肿瘤诊断中可产生较高的AUC。AIS在BI-RADS分类分层中很重要。
