关键词: 乳腺肿瘤, 磁共振成像, 乳腺影像报告和数据系统, 诊断，鉴别

Abstract:

Objective To explore the value of the prediction model based on multimodal MRI characteristics for the differential diagnosis of benign and malignant breast tumors of breast imaging reporting and date system （BI-RADS） 4 types. Methods A total of 204 patients with BI-RADS 4 types of breast tumors confirmed by contrast-enhanced MRI in 3201 Hospital from January 2018 to January 2023 were retrospectively included， and were divided into the malignant group （124 cases） and the benign group （80 cases） according to surgical histopathology. Clinical and MRI imaging characteristics of the two groups were compared. Multivariate logistic regression analysis was performed for the differential diagnosis indexes of benign and malignant breast tumors of BI-RADS 4 types. A prediction model for differential diagnosis of benign and malignant BI-RADS 4 types of breast tumors was constructed. Receiver operator characteristic （ROC） curve was used to compare the differential diagnostic value of each index for benign and malignant BI-RADS 4 types of breast tumors. Results There were statistically significant differences in age （t=7.78， P<0.001）， internal enhancement type （χ²=14.50， P=0.002）， apparent diffusion coefficient （t=-6.77， P<0.001） longitudinal relaxation time （T₁） value （t=-6.15， P<0.001）， and longitudinal relaxation rate （R₁） value （t=7.02， P<0.001） between the malignant and benign groups. Multivariate analysis showed that age （OR=1.16， 95%CI： 1.07-1.25， P<0.001）， internal reinforcement type （uneven： OR=8.08， 95%CI： 2.21-29.51， P=0.002）， apparent diffusion coefficient （OR=0.01， 95%CI： 0.00-0.05， P<0.001）， T₁ value （OR=0.99， 95%CI： 0.99-1.00， P<0.001）， and R₁ value （OR=1 043.50， 95%CI： 46.48-2 3426.36， P<0.001） were all independent factors influencing the differential diagnosis of benign and malignant BI-RADS 4 types of breast tumors. According to the results of multivariate analysis， a logistic regression model for differential diagnosis of benign and malignant BI-RADS 4 types of breast tumors was constructed. logit （P）=0.05+0.15×age+2.09×internal enhancement type-5.21×apparent diffusion coefficient-0.01×T₁ value+6.95×R₁ value. ROC curve analysis showed that age， internal reinforcement type， apparent diffusion coefficient， T₁ value， R₁ value， and logistic regression model P-value were used for differential diagnosis of benign and malignant breast tumors， the Jordan indexes were 40.60%， 39.68%， 49.44%， 38.23%， 43.27%， and 75.70%， respectively. The areas under the ROC curve were 0.757， 0.647， 0.718， 0.724， 0.757， and 0.924， respectively. Conclusion Multimodal magnetic resonance indexes， including internal reinforcement type， apparent diffusion coefficient， T₁ value and R₁ value， can be used for the differential diagnosis of benign and malignant BI-RADS 4 types of breast tumors. The differential diagnostic model based on the above indexes has good differential diagnostic efficacy for benign and malignant BI-RADS 4 types of breast tumors.

Key words: Breast neoplasms, Magnetic resonance imaging, Breast imaging reporting and data system, Diagnosis， differential