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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
Published on: January 8, 2018
Scoring system based on BI-RADS lexicon to predict probability of malignancy in suspicious microcalcifications
Ji Hyun Youk1, Eun Ju Son, Jeong-Ah Kim
1Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
Purpose:
To develop a scoring system allowing quantification of the probability of malignancy of suspicious microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS).
Methods:
A total of 163 microcalcification lesions surgically excised in 147 women (aged 28-65 years) were included. Two radiologists independently reviewed each lesion with BI-RADS (4th edition). The interobserver agreement and positive predictive value (PPV) for each descriptor were determined and multivariate analysis was used to develop a scoring system. The scores were compared between benign and malignant lesions and among BI-RADS categories by using the two-sample t-test or the analysis of variance. To assess the discriminative power of the scoring system, the area under the receiver-operating characteristic curve (AUC) and cutoff values for categorization were determined. For the test of the scoring system, the validation data set from a different facility was applied.
Results:
Interobserver agreement was fair to moderate for distribution, morphology, and category (κ = 0.45, 0.40, and 0.37). PPVs were significantly different among BI-RADS descriptors and categories (P < 0.0001). Of the scoring system developed, the AUC was 0.75. The scores between benign and malignant lesions were significantly different. By means of cutoff values, PPV in category 4a, 4b, 4c, and 5 was 7.0%, 15.0%, 44.8%, and 83.3%, respectively. For the validation data set, the AUC was 0.79 and the PPV in category 4a, 4b, 4c, and 5 was 9.4%, 24.1%, 62.5%, and 80.0%, respectively.
Conclusions:
In suspicious microcalcifications, our scoring system based on BI-RADS (4th edition) could help to derive a specific final category with good stratification of the probability of malignancy.