# Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis

## TL;DR

This study evaluates the consistency of bone density and texture measurements from minimally post-processed knee radiographs and their ability to distinguish osteoarthritis and BMLs, showing promising diagnostic potential.

## Contribution

It demonstrates that bone characteristics from minimally processed radiographs are reliable and can differentiate between healthy and osteoarthritic knees using machine learning.

## Key findings

- High correlation (0.94-0.97) in bone density between minimal and clinical post-processing.
- Bone texture differences are significant between controls and osteoarthritis/BMLs.
- Machine learning classification achieved up to 85% ROC AUC in identifying BMLs.

## Abstract

Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between radiographs with minimal and clinical post-processing, and to compare the differences in bone characteristics between controls and subjects with knee osteoarthritis or medial tibial bone marrow lesions (BMLs). Tibial bone density and texture was evaluated from radiographs with both minimal and clinical post-processing in 109 subjects with and without osteoarthritis. Bone texture was evaluated using fractal signature analysis. Significant correlations (p<0.001) were found in all regions (between 0.94 and 0.97) for calibrated bone density between radiographs with minimal and clinical post-processing. Correlations varied between 0.51 and 0.97 (p<0.001) for FD_Ver texture variable and between -0.10 and 0.97 for FD_Hor. Bone density and texture were different (p<0.05) between controls and subjects with osteoarthritis or BMLs mainly in medial tibial regions. When classifying healthy and osteoarthritic subjects using a machine learning-based elastic net model with bone characteristics, area under the receiver operating characteristics (ROCAUC) curve was 0.77. For classifying controls and subjects with BMLs, ROCAUC was 0.85. In conclusion, differences in bone density and texture can be assessed from knee radiographs when using minimal post-processing.

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Source: https://tomesphere.com/paper/1902.02098