为了评估数字乳腺 X 线摄影 (DM) 和数字乳腺断层合成 (DBT) 的诊断性能，DM 将 DBT 与基于 AI 的乳腺肿块策略相结合，用于乳腺肿块≤ 2 cm。自 2018 年 11 月起，采集了 483 名患者（包括 512 个乳腺肿块）的 DM 和 DBT 图像。至2019年11月。通过组织学分析和24个月内随访的活检来确定恶性肿瘤和良性肿瘤。采用放射组学和深度学习方法提取图像中的乳腺肿块特征，最终进行良恶性分类。将 DM、DBT 和 DM 组合 DBT (DM DBT) 图像分别输入放射组学和深度学习模型以构建相应的模型。采用受试者工作特征曲线下面积 (AUC) 来估计模型性能。招募了另一中心2021年3月至2022年12月的146名患者的外部数据集进行外部验证。在内部测试数据集中，与DM模型和DBT模型相比，基于放射组学和深度学习的DM DBT模型均表现出统计数据AUC 显着升高 [0.810 (RA-DM)、0.823 (RA-DBT) 和 0.869 (RA-DM DBT)，P ≤ 0.001； 0.867 (DL-DM)、0.871 (DL-DBT) 和 0.908 (DL-DM DBT)，P = 0.001]。在仅使用 DM（0.867 vs 0.810，P = 0.001）、仅使用 DBT（0.871 vs 0.823，P = 0.001）和 DM DBT（0.908 vs 0.869，P = 0.003）的实验中，深度学习模型优于放射组学模型。与仅 DM 相比，DBT 对于诊断小于 2 厘米的乳房肿块具有明显的附加价值。基于人工智能的方法，尤其是深度学习，可以帮助实现卓越的性能。版权所有 © 2024。由 Elsevier Ltd 出版。

To evaluate the diagnosis performance of digital mammography (DM) and digital breast tomosynthesis (DBT), DM combined DBT with AI-based strategies for breast mass ≤ 2 cm.DM and DBT images in 483 patients including 512 breast masses were acquired from November 2018 to November 2019. Malignant and benign tumours were determined by biopsies using histological analysis and follow-up within 24 months. The radiomics and deep learning methods were employed to extract the breast mass features in images and finally for benign and malignant classification. The DM, DBT and DM combined DBT (DM + DBT) images were fed into radiomics and deep learning models to construct corresponding models, respectively. The area under the receiver operating characteristic curve (AUC) was employed to estimate model performance. An external dataset of 146 patients from March 2021 to December 2022 from another center was enrolled for external validation.In the internal testing dataset, compared with the DM model and the DBT model, the DM + DBT models based on radiomics and deep learning both showed statistically significant higher AUCs [0.810 (RA-DM), 0.823 (RA-DBT) and 0.869 (RA-DM + DBT), P ≤ 0.001; 0.867 (DL-DM), 0.871 (DL-DBT) and 0.908 (DL-DM + DBT), P = 0.001]. The deep learning models present superior to the radiomics models in the experiments with only DM (0.867 vs 0.810, P = 0.001), only DBT (0.871 vs 0.823, P = 0.001) and DM + DBT (0.908 vs 0.869, P = 0.003).DBT has a clear additional value for diagnosing breast mass less than 2 cm compared with only DM. AI-based methods, especially deep learning, can help achieve excellent performance.Copyright © 2024. Published by Elsevier Ltd.