Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification

Abstract

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models’ clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

“…Firstly, the prevalence of breast cancer in our study population is higher than that of the general population, leading to a selection bias. Actually, considering that the presence of unbalanced datasets leads a ML classifier to classify the most represented class better, to obtain a predictive model capable of accurately classifying two different classes, there is the need to have a balanced dataset (i.e., benign and malignant) [ 35 ]. Furthermore, as secondary referral centers for breast cancer, our population might present a higher number of malignancies in comparison with the general population.…”

“…The reason why we used shallow (not deep) machine learning techniques is twofold. The first is the need for explainability: shallow learning and explainable methods provide insights into the features driving their decisions, allowing clinicians to understand the reasoning behind the system’s recommendations [ 35 ]. The second is the training in small data scenarios: deep learning methods are well known to require huge amounts of data for training.…”