Scoring System to Predict Malignancy for MRI-Detected Lesions in Breast Cancer Patients: Diagnostic Performance and Effect on Second-Look Ultrasonography
10.3348/jksr.2020.81.2.379
- Author:
Young Geol KWON
1
;
Ah Young PARK
Author Information
1. Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Korea
- Publication Type:Original Article
- From:Journal of the Korean Radiological Society
2020;81(2):379-394
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
Purpose:To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients.
Materials and Methods:Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's t-test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category.
Results:Lesion size ≥ 8 mm (p < 0.001), location in the same quadrant as the primary cancer (p = 0.005), delayed plateau kinetics (p = 0.010), T2 isointense (p = 0.034) and hypointense (p = 0.024) signals, and irregular mass shape (p = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided.
Conclusion:The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.
