基于泰国人群的乳腺癌筛查模型，在多中心验证中表现优异。
Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population
- 用改进的EfficientNetV2加注意力机制，提升乳腺影像识别能力。
- 在三组数据上检测准确率（AUROC）达0.89至0.96，局部定位精准。
- 医生认可度高，系统可用性评分超70，适合临床辅助诊断使用。
本研究提出一种基于深度学习的乳腺癌检测系统，采用改进的EfficientNetV2架构并引入增强注意力机制。模型在泰国主要医疗中心的乳腺钼靶图像上训练，并在三个独立数据集上进行验证：同域测试集（9,421例）、活检确诊集（883例）和跨域泛化集（761例）。在癌症检测任务中，模型在三组数据上的AUROC分别为0.89、0.96和0.94。通过病变定位分数（LLF）与非病灶定位分数（NLF）评估，系统展现出强病变定位能力。临床一致性测试显示，对活检确诊病例分类与定位一致率分别达到83.5%和84.0%，对跨域病例分别为78.1%和79.6%。专家放射科医生对活检病例的接受率达96.7%，跨域病例为89.3%。系统可用性量表得分分别为74.17（原医院）和69.20（验证医院），表明其具备良好临床接受度。结果证明该模型在辅助乳腺钼靶判读方面具有实际应用潜力。
原文摘要 · Abstract (English)
This study presents a deep learning system for breast cancer detection in mammography, developed using a modified EfficientNetV2 architecture with enhanced attention mechanisms. The model was trained on mammograms from a major Thai medical center and validated on three distinct datasets: an in-domain test set (9,421 cases), a biopsy-confirmed set (883 cases), and an out-of-domain generalizability set (761 cases) collected from two different hospitals. For cancer detection, the model achieved AUROCs of 0.89, 0.96, and 0.94 on the respective datasets. The system's lesion localization capability, evaluated using metrics including Lesion Localization Fraction (LLF) and Non-Lesion Localization Fraction (NLF), demonstrated robust performance in identifying suspicious regions. Clinical validation through concordance tests showed strong agreement with radiologists: 83.5% classification and 84.0% localization concordance for biopsy-confirmed cases, and 78.1% classification and 79.6% localization concordance for out-of-domain cases. Expert radiologists' acceptance rate also averaged 96.7% for biopsy-confirmed cases, and 89.3% for out-of-domain cases. The system achieved a System Usability Scale score of 74.17 for source hospital, and 69.20 for validation hospitals, indicating good clinical acceptance. These results demonstrate the model's effectiveness in assisting mammogram interpretation, with the potential to enhance breast cancer screening workflows in clinical practice.
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