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Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.
- Source :
-
Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Jun; Vol. 37 (3), pp. 1038-1053. Date of Electronic Publication: 2024 Feb 13. - Publication Year :
- 2024
-
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.<br /> (© 2024. The Author(s).)
- Subjects :
- Humans
Female
Breast diagnostic imaging
Breast pathology
Machine Learning
Radiographic Image Interpretation, Computer-Assisted methods
Support Vector Machine
Breast Diseases diagnostic imaging
Breast Diseases pathology
Breast Diseases diagnosis
Breast Diseases classification
Radiomics
Calcinosis diagnostic imaging
Calcinosis pathology
Mammography methods
Breast Neoplasms diagnostic imaging
Breast Neoplasms pathology
Breast Neoplasms diagnosis
Subjects
Details
- Language :
- English
- ISSN :
- 2948-2933
- Volume :
- 37
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Journal of imaging informatics in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 38351223
- Full Text :
- https://doi.org/10.1007/s10278-024-01012-1