A New Nomogram For Predicting The Malignant Diagnosis Of Breast Imaging Reporting And Data System (Bi-Rads) Ultrasonography Category 4a Lesions In Women With Dense Breast Tissue In The Diagnostic Setting

Background: Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety.Methods: A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation.Results: The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR=4.588, P=0.004), US features [margin (OR=2.916, P=0.019), shape (irregular vs. oval, OR=2.474, P=0.044; round vs. oval, OR=1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR=7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR=6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups.Conclusions: We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.