Radiomics grading of hand osteoarthritis severity using standard radiographs: Results from the DIGICOD cohort.

OBJECTIVE

This study evaluated whether radiomics analysis of standard hand radiographs could provide an automated and objective assessment of structural severity in hand osteoarthritis (HOA) compared to Kellgren-Lawrence (KL) scores, ranging from 0 (normal) to 4 (severe).

DESIGN

We conducted a retrospective study using baseline data from the DIGICOD (DIGital Cohort Osteoarthritis Design) cohort, including patients with HOA. Standard posteroanterior radiographs were segmented semi-automatically using a U-Net model with manual correction. Radiomics features describing intensity, shape, and texture were extracted for each joint and reduced after filtering and correlation suppression. The cohort was split into training (80%) and test sets (20%) using stratified sampling. Random Forest classifiers were trained to detect structural involvement (KL ≥2), severe disease (KL 3-4), and predict multiclass KL grades (0-1, 2, 3, 4) at the joint level.

RESULTS

379 radiographs were analyzed. Detection of structural involvement (KL ≥2) achieved an AUC of 0.81 [95% CI: 0.79-0.83], with high sensitivity (89%) but low specificity (59%). Severe disease detection (KL 3-4) reached an AUC of 0.83 [95% CI: 0.81-0.85], with very good sensitivity (80%) and good specificity (70%). Multiclass KL prediction showed lower performance (macro-averaged AUC 0.76; accuracy 56%), reflecting challenges in distinguishing intermediate (KL 2-3) and severe (KL 4) grades.

CONCLUSIONS

This is the first study applying radiomics to standard hand radiographs for automated and objective scoring of radiographic severity in HOA. The model showed good performance detecting structural damage, supporting radiomics as a potential tool to reduce reliance on subjective visual grading.