Ultrasound intelligent diagnostic system combined with mammography for differentiating benign and malignant non-mass breast lesions
10.13929/j.issn.1672-8475.2025.05.008
- VernacularTitle:超声智能诊断系统联合钼靶X线检查鉴别乳腺良、恶性非肿块型病变
- Author:
Renxu LI
1
;
Jingyun WU
;
Xun KONG
;
Luzeng CHEN
Author Information
1. 北京大学第一医院超声医学科,北京 100034
- Publication Type:Journal Article
- Keywords:
breast diseases;
ultrasonography;
artificial intelligence;
mammography
- From:
Chinese Journal of Interventional Imaging and Therapy
2025;22(5):336-340
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of ultrasound intelligent diagnostic system combined with mammography for differentiating benign and malignant non-mass breast lesions(NMBL).Methods Totally 107 patients with NMBL were retrospectively enrolled,including 64 cases of malignant(malignant group)and 43 cases of benign lesions(benign group).Clinical,routine ultrasound,ultrasound intelligent diagnostic system(artificial intelligence[AI]system)and mammography data were compared between groups.Logistic regression analysis was performed,receiver operating characteristic(ROC)curves were drawn,the areas under the curves(AUC)were calculated,and the efficacy of AI system combined with mammography for differentiating benign and malignant NMBL was analyzed.Results Significant differences of the maximum diameter of lesion,ratio of axillary lymph node enlargement and suspected malignant calcification on mammography,as well as of AI system malignancy risk and AI system breast imaging reporting and data system(BI-RADS)classification were found between groups(all P＜0.05).AI system binary classification was obtained based on AI system malignancy risk.The AUC of suspected malignant calcification on mammography,AI system BI-RADS classification and AI system binary classification for differential diagnosis of benign and malignant NMBL was 0.840,0.810 and 0.817,respectively,while of suspected malignant calcification on mammography combined with AI system BI-RADS classification or AI system binary classification were both 0.856,higher than that of AI system BI-RADS classification/AI system binary classification alone(both P＜0.05)but not significantly different with that of suspected malignant calcification on mammography alone(both P＞0.05).Logistic regression analysis of age,the maximum diameter of lesion,axillary lymph node enlargement and suspected malignant calcification on mammography combined with AI system malignancy risk(model 1),AI system BI-RADS classification(model 2)or AI system binary classification(model 3)showed that suspected malignant calcification on mammography,AI system malignancy risk,AI system BI-RADS classification and AI system binary classification were all independent risk factors of malignant NMBL(all P＜0.05),and AUC of model 1,2 and 3 for differentiating benign and malignant NMBL was 0.966,0.964 and 0.957,respectively.Conclusion Ultrasound intelligent diagnostic system combined with mammography was helpful for differentiating benign and malignant NMBL.Combining with clinical indicators might improve diagnostic efficacy.
