Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4).
BMC Med Imaging
; 23(1): 182, 2023 11 10.
Article
en En
| MEDLINE
| ID: mdl-37950164
ABSTRACT
BACKGROUND:
This study aims to providing a reliable method that has good compliance and is easy to master to improve the accuracy of NMLE diagnosis.METHODS:
This study retrospectively analyzed 122 cases of breast non-mass-like enhancement (NMLE) lesions confirmed by postoperative histology. MRI features and clinical features of benign and malignant non-mass enhancement breast lesions were compared by using independent sample t test, χ2test and Fisher exact test. P < 0.05 was considered statistically significant. Statistically significant parameters were then included in logistic regression analysis to build a multiparameter differential diagnosis modelto subdivide the BI-RADS Category 4.RESULTS:
The distribution (odds ratio (OR) = 8.70), internal enhancement pattern (OR = 6.29), ADC value (OR = 5.56), and vascular sign (OR = 2.84) of the lesions were closely related to the benignity and malignancy of the lesions. These signs were used to build the MRI multiparameter model for differentiating benign and malignant non-mass enhancement breast lesions. ROC analysis revealed that its optimal diagnostic cut-off value was 5. The diagnostic specificity and sensitivity were 87.01% and 82.22%, respectively. Lesions with 1-6 points were considered BI-RADS category 4 lesions, and the positive predictive values of subtypes 4a, 4b, and 4c lesions were15.79%, 31.25%, and 77.78%, respectively.CONCLUSIONS:
Comprehensively analyzing the features of MRI of non-mass enhancement breast lesions and building the multiparameter differential diagnosis model could improve the differential diagnostic performance of benign and malignant lesions.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Imagen por Resonancia Magnética
Límite:
Female
/
Humans
Idioma:
En
Revista:
BMC Med Imaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2023
Tipo del documento:
Article