Nomogram for reducing unnecessary biopsies of breast lesions based on MRI and clinical features: a multi-center...

The Breast Imaging Reporting and Data System (BI-RADS) is a widely accepted standardized framework for breast imaging interpretation including ultrasound, mammogram and magnetic resonance. Intermediate BI-RADS categories nodules currently require further biopsy or surgical resection to obtain pathological information. Notably, many such nodules are ultimately diagnosed as benign, prompting us to question whether intermediate BI-RADS categories nodules truly need invasive procedures. Additionally, malignancy rates of intermediate BI-RADS nodules vary across age groups and are influenced by clinical/biochemical factors. Therefore, a pressing challenge is to leverage current diagnostic tools for more precise identification of nodules that truly require biopsy, thereby reducing unnecessary invasive interventions. This study aims to address these challenges by integrating radiomics features with clinical and biochemical data to improve diagnostic accuracy.

This retrospective study enrolled 384 breast nodule patients from two medical centers with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and blood biochemical tests, allocated into training and external test sets. A total of 3,948 radiomic features were extracted from DCE-MRI images and integrated with clinical characteristics. After 5-fold cross-validation for high-frequency feature selection, a malignancy-predicting nomogram was developed. Diagnostic performance was evaluated via area under receiver operator characteristic curve (AUC) with DeLong test against BI-RADS. Under the sensitivity threshold of > 95%, McNemar's test compared the specificity between the nomogram and BI-RADS to evaluate their biopsy reduction capabilities.

The nomogram yielded an AUC of 0.89 [95% confidence interval (CI), 0.85-0.92] in the training cohort and an AUC of 0.89 (95% CI, 0.81-0.96) in the test cohort. When applying the cut-off value with ≥ 95% sensitivity, the nomogram can reduce unnecessary biopsies by 12.8% (16/125) in the training cohort and 25% (9/36) in the test cohort when compared with BI-RADS (p = 0.068 in training cohort and p = 0.078 in test cohort).

We have established a nomogram based on DCE-MRI radiomics and clinical risk factors to distinguish malignant from benign breast lesions, and demonstrated potential to reduce unnecessary biopsies, serving as a supplementary tool for BI-RADS-based clinical decision-making.