What question did this study set out to answer?

The study aims to create a machine learning model to improve discrimination between benign and malignant breast microcalcifications classified as BI-RADS 4 and 5.

June 19, 2026Open Access

Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant–Benign Discrimination

Key Points

The study aims to create a machine learning model to improve discrimination between benign and malignant breast microcalcifications classified as BI-RADS 4 and 5.
Evaluated 53 biopsy-confirmed cases with clinical and radiological variables.
Used various machine learning algorithms including Logistic Regression and AdaBoost for model development and optimization.
Measured performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
Logistic Regression achieved an accuracy of 0.909 and an F1-score of 0.889.
AdaBoost recorded a perfect recall of 1.000 in internal evaluation.
Findings are preliminary and necessitate larger multicenter studies for validation.

Abstract

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation.

Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant–Benign Discrimination

Key Points

Abstract

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