Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT

TL;DR

This study presents an automated CT-based classification system for differentiating osteoporotic and neoplastic vertebral compression fractures, aiding treatment planning through quantitative morphologic and bone density analysis.

Contribution

Introduces a novel fracture classification system utilizing automated measurements and machine learning to distinguish fracture types from longitudinal CT data.

Findings

Achieved over 81% accuracy with combined features.

Automated measurements effectively differentiate fracture origins.

Longitudinal data improves classification performance.

Abstract

Classification of vertebral compression fractures (VCF) having osteoporotic or neoplastic origin is fundamental to the planning of treatment. We developed a fracture classification system by acquiring quantitative morphologic and bone density determinants of fracture progression through the use of automated measurements from longitudinal studies. A total of 250 CT studies were acquired for the task, each having previously identified VCFs with osteoporosis or neoplasm. Thirty-six features or each identified VCF were computed and classified using a committee of support vector machines. Ten-fold cross validation on 695 identified fractured vertebrae showed classification accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and combined feature sets respectively.