Comparison of resampling methods and radiomic machine learning classifiers for predicting bone quality using dual-energy X-ray absorptiometry

• Autores: Gonzalez, Mailen; Fuertes García, José Manuel; Zanchetta, María Belén; Abdala, Ruben; Massa, José María
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: DATA RESAMPLING; DUAL ENERGY X-RAY ABSORPTIOMETRY; MACHINE LEARNING; RADIOMICS; TRABECULAR BONE SCORE
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Background/Objectives: This study presents a novel approach, based on a combination of radiomic feature extraction, data resampling techniques, and machine learning algorithms, for the detection of degraded bone structures in Dual X-ray Absorptiometry (DXA) images. This comprehensive approach, which addresses the critical aspects of the problem, distinguishes this work from previous studies, improving the performance achieved by the most similar studies. The primary aim is to provide clinicians with an accessible tool for quality bone assessment, which is currently limited. Methods: A dataset of 1531 spine DXA images was automatically segmented and labelled based on Trabecular Bone Score (TBS) values. Radiomic features were extracted using Pyradiomics, and various resampling techniques were employed to address class imbalance. Three machine learning classifiers (Logistic Regression, Support Vector Machine (SVM), and XGBoost) were trained and evaluated using standard performance metrics. Results: The SVM classifier outperformed the other classifiers. The highest F-score of 97.5% was achieved using the Grey Level Dependence Matrix and Grey Level Run Length Matrix feature combination with SMOTEENN resampling, which proved to be the most effective resampling technique, while the undersampling method yielded the lowest performance. Conclusions: This research demonstrates the potential of radiomic texture features, resampling techniques, and machine learning methods for classifying DXA images into healthy or degraded bone structures, which potentially leads to improved clinical diagnosis and treatment.
Fil: Gonzalez, Mailen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina
Fil: Fuertes García, José Manuel. Universidad de Jaén; España
Fil: Zanchetta, María Belén. Instituto de Diagnostico E Investigaciones Metabolicas (idim);
Fil: Abdala, Ruben. Instituto de Diagnostico E Investigaciones Metabolicas (idim);
Fil: Massa, José María. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Investigaciones en Tecnología Informática Avanzada; Argentina