The Added Role of Kaiser Score in Evaluating BIRADS 4 Breast Lesions in Breast MRI

Abstract

Abstract Background Breast malignancy is one of the most common malignancies affecting women, representing 31% of overall tumors affecting the females around the world. The increase in breast cancer incidence is accompanied by an increase in the clinician and researchers concerns regarding the improvement of diagnostic and therapeutic tools. Dynamic contrast- enhanced magnetic resonance imaging (MRI) is one of the most sensitive breast imaging modalities, but it has a relatively low specificity. Breast MRI findings are evaluated using the standardized American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon, which uses multiple descriptors to characterize enhancing lesions. Kaiser score is a simple flowchart that includes five major diagnostic criteria (root sign, dynamic enhancement curve type, margins, internal enhancement pattern, and edema) and its diagnostic accuracy is high. Aim of the Work The study aims to evaluate the added role of Kaiser score in score in evaluating BIRADS 4 breast lesions in breast MRI. Patients and Methods This is a retrospective study included 22 female patients who were diagnosed with breast lesions categorized as BIRADS 4, using the Kaiser score, where the Kaiser score threshold for malignancy is ≤ 4. Results The study investigated the diagnostic performance of Kaiser score in BIRADS 4 breast lesions. Kaiser score demonstrated 93 % sensitivity and 71.43 % specificity and 86.36% accuracy in comparison to the histopathology which is the gold standard investigation. Conclusion Kaiser score is a simple classification model which follows an intuitive tree structure reflecting structured step by step diagnostic process for lesion differentiation, includes five major diagnostic criteria (root sign, dynamic enhancement curve type, margins, internal enhancement pattern, and oedema) and it can be reliable in predicting malignant breast lesions due to its high accuracy, which can decrease the percentage of unnecessary breast biopsies. This has a positive potential to impact healthcare costs, as well as patient concern.