Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography

• Published in: Diagnostics (Basel) Vol. 15; no. 8; p. 953
• Main Authors: Stefano, Alessandro, Bini, Fabiano, Giovagnoli, Eleonora, Dimarco, Mariangela, Lauciello, Nicolò, Narbonese, Daniela, Pasini, Giovanni, Marinozzi, Franco, Russo, Giorgio, D’Angelo, Ildebrando
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.04.2025; MDPI
• Subjects: Accuracy; Algorithms; Asymptomatic; automated diagnostic systems; Breast cancer; breast lesion classification; Calcification; Cancer; Cancer therapies; Classification; Datasets; Deep learning; Diagnosis; Health aspects; Italy; Learning strategies; Machine learning; Magnetic resonance imaging; Mammography; Medical imaging equipment; Medical prognosis; Mortality; Neural networks; Oncology, Experimental; Patients; Radiomics; Risk assessment; Tomography; Italy; automated diagnostic systems; deep learning; radiomics; mammography; breast lesion classification
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15080953

Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation by radiologists is complex and subject to variability, emphasizing the need for automated diagnostic tools to enhance accuracy and efficiency. This study compares a radiomics workflow based on machine learning (ML) with a deep learning (DL) approach for classifying breast lesions as benign or malignant. Methods: matRadiomics was used to extract radiomics features from mammographic images of 1219 patients from the CBIS-DDSM public database, including 581 cases of microcalcifications and 638 of masses. Among the ML models, a linear discriminant analysis (LDA) demonstrated the best performance for both lesion types. External validation was conducted on a private dataset of 222 images to evaluate generalizability to an independent cohort. Additionally, a deep learning approach based on the EfficientNetB6 model was employed for comparison. Results: The LDA model achieved a mean validation AUC of 68.28% for microcalcifications and 61.53% for masses. In the external validation, AUC values of 66.9% and 61.5% were obtained, respectively. In contrast, the EfficientNetB6 model demonstrated superior performance, achieving an AUC of 81.52% for microcalcifications and 76.24% for masses, highlighting the potential of DL for improved diagnostic accuracy. Conclusions: This study underscores the limitations of ML-based radiomics in breast cancer diagnosis. Deep learning proves to be a more effective approach, offering enhanced accuracy and supporting clinicians in improving patient management.