数据与计算发展前沿2024，Vol.6Issue(2)：68-79,12.DOI:10.11871/jfdc.issn.2096-742X.2024.02.007
基于深度学习乳腺X线摄影钙化识别分类模型的临床应用价值
Assessing the Clinical Utility of a Deep Learning-Based Model for Calcification Recognition and Classification in Mammograms
摘要
Abstract
[Objective]This article is to assess the clinical application value of a deep learning-based model for recognizing and classifying mammography calcifications.[Methods]Multicenter mammography data were employed,with lesion assessments conducted by both senior-junior radiologists and two junior radiologists.The deep learning-based model was used in both standalone and combined approaches.Diagnostic performance was then evaluated.[Results]The introduction of the deep learning model demonstrates comparable capabilities to senior-junior radi-ologists and two junior radiologists(miss rates:0.81%vs.0.65%,1.14%vs.1.63%,P>0.05).The deep learning model effectively assists senior-junior radiologists(sensitivity 0.926,AUC 0.81,P=0.014)and two junior radiolo-gists(sensitivity 0.896,AUC 0.79,P=0.049)in detecting suspicious calcifications,especially in benign lesions.[Limitations]The study requires more prospective multicenter data and different deep learning models to com-pare their clinical utility.[Conclusions]Deep learning frameworks offer valuable support for mammography cal-cification recognition and classification,providing rapid assistance for diagnosis and clinical strategy support.关键词
乳腺病变/乳腺X线摄影术/钙化识别/深度学习Key words
breast lesions/mammography/calcification recognition/deep learning引用本文复制引用
袁家琳,欧阳汝珊,戴懿,赖小慧,马捷,龚静山..基于深度学习乳腺X线摄影钙化识别分类模型的临床应用价值[J].数据与计算发展前沿,2024,6(2):68-79,12.基金项目
国家自然科学基金面上项目"面向数据不确定性的多模态医学影像分析理论和方法"(62276121) （62276121）
广东省医学科研基金"基于深度学习的乳腺X线摄影与自然语言处理技术对乳腺内潜在恶性病变的分层评估研究"(A2024506) （A2024506）
深圳市科技创新委员会国际科技自主合作项目"基于多模态深度学习算法对乳腺癌筛查和诊断评估系统研究"(GJHZ20220913142613025) （GJHZ20220913142613025）
