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Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification. | LitMetric

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

J Imaging Inform Med

Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.

Published: June 2024

AI Article Synopsis

  • - Breast microcalcifications appear in 80% of mammograms and can potentially indicate invasive tumors, but accurately diagnosing them is challenging due to their varying characteristics.
  • - This study introduces a radiomic signature that distinguishes between healthy tissue, benign, and malignant microcalcifications by extracting features from a dataset of 756 regions of interest (ROIs) and employing machine learning classifiers.
  • - The models, particularly XGBoost, showed strong performance metrics, with AUC-ROC scores indicating good classification accuracy, while key radiomic features highlighted in the study align with findings from other breast cancer research.

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11169144PMC
http://dx.doi.org/10.1007/s10278-024-01012-1DOI Listing

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