Value analysis of the prediction model based on multimodal MRI characteristics for the differential diagnosis of benign and malignant BI-RADS 4 types of breast tumors
10.3760/cma.j.cn371439-20231109-00115
- VernacularTitle:基于多模态MRI特征构建的预测模型用于BI-RADS 4类乳腺肿瘤良恶性鉴别诊断价值分析
- Author:
Bin ZHU
1
;
Tao WAN
;
Hua XU
;
Hao JIA
;
Shixin CHEN
Author Information
1. 三二〇一医院影像科，汉中 723000
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Breast imaging reporting and data system;
Diagnosis, differential
- From:
Journal of International Oncology
2024;51(11):678-683
- CountryChina
- Language:Chinese
-
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.
