分子影像学杂志2024，Vol.47Issue(1)：25-30,6.DOI:10.12122/j.issn.1674-4500.2024.01.05
基于深度学习的乳腺X线辅助诊断系统对乳腺钙化检出和良恶性分类的临床价值
Clinical value of a deep learn-based mammography assisted diagnosis system for breast calcification detection and benign and malignant classification
摘要
Abstract
Objective To investigate the clinical value of the deep learning-based mammography-assisted diagnosis(DL)system for breast calcification detection and benign and malignant classification.Methods A retrospective analysis was performed on the craniocaudal and internal and external oblique imaging data of 400 patients who underwent bilateral mammography in Xuzhou Central Hospital from January 2020 to December 2022.The unanimous judgment of two associate chief physicians with more than 15 years of experience in mammography diagnosis was used as the standard group,the images were blinded and independently reviewed by 1 junior resident,1 senior attending physician,and the DL system,respectively.After a 4-week washout period,the images were blinded and independently reviewed by the combined model(junior resident+DL system)again.Combined with two-way table chi-square test,the effects of different ACR types,morphology and distribution of calcification,and BI-RADS classification on the detection of calcification were evaluated.The area under the curve(AUC)was used to evaluate the difference in the detection of suspicious calcification among junior residents,senior attending physician,DL system and combined model(junior resident+DL system).Results A total of 975 suspicious calcifications of BI-RADS3 grade and above were detected in 1600 images(400 patients).The sensitivities of junior resident A,senior attending physician B,DL system and combined model were 81.95%,96.62%,93.03%and 96.41%,respectively.The sensitivity of senior attending physician B,DL system and combined model to calcification detection was not affected by breast ACR type,morphology and distribution of calcification,and BI-RADS classification,while the sensitivity of junior resident A was affected by it.The combined model(junior resident + DL system)had high AUC value,sensitivity and specificity in predicting the benign and malignant nature of calcifications,with 0.891,90.0%and 88.2%,respectively,which differed from that of the junior resident(P<0.01).With the help of the DL system,the diagnostic performance of the junior resident was significantly improved,and the AUC value increased from 0.740 to 0.891.Conclusion The DL system is highly sensitive to the detection of suspicious calcifications of BI-RADS 3 grade and above,and has a high classification performance of benign and malignant calcifications,which is comparable to that of senior attending physician.With the help of the DL system,the junior resident can reduce the missed diagnosis of calcification and misdiagnosis,and improve the accuracy of breast cancer screening and diagnosis.关键词
乳腺X线摄影/可疑钙化/深度学习/乳腺癌/人工智能Key words
mammography/suspicious calcification/deep learning/breast cancer/artificial intelligence引用本文复制引用
翟天旭,张敏伟,张子秋,孔德懿,李德春..基于深度学习的乳腺X线辅助诊断系统对乳腺钙化检出和良恶性分类的临床价值[J].分子影像学杂志,2024,47(1):25-30,6.基金项目
江苏省十四五医学重点学科项目(ZDXK202237) （ZDXK202237）
徐州市科学技术局社会发展项目(KC15SH061) （KC15SH061）
