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Machine Learning Integrating Quantitative MRI and Clinical Features Predicts Loss of Ambulation in Duchenne Muscular Dystrophy

Year: 2026

Type: article

Abstract: BACKGROUND: Loss of ambulation (LOA) is a key milestone in Duchenne muscular dystrophy (DMD) with highly variable timing, complicating clinical counselling and trial design. Quantitative MRI non-invasively assesses muscle fat infiltration, yet few models have integrated MRI with clinical variables for individualized LOA prediction. We aimed to develop and validate a machine-learning model integrating quantitative MRI and clinical features to predict time to LOA and to derive interpretable, individualized risk estimates and candidate thresholds. METHODS: This prospective study enrolled 188 boys with DMD (median age 9.10 years). Quantitative MRI parameters (T1, T2, fat fraction [FF]) of 19 hip/thigh muscles and clinical variables were measured. Patients were randomly split into training (80%) and test (20%) sets. Feature selection used LASSO, Boruta and recursive feature elimination based on random survival forest (RSF). Three survival models were compared: RSF and a ridge-penalised Cox model (both integrating clinical and MRI-derived features) and a multivariable Cox model restricted to clinical variables. Performance was assessed by concordance index (C-index), time-dependent area under the curve (AUC), integrated Brier score (IBS) and decision curve analysis at 12, 24, 36 and 48 months. RESULTS: Over median 43.5 months follow-up, 66 patients (35.1%) experienced LOA. Six predictors were retained: NSAA score, MRC%, age and three FF parameters (rectus femoris, vastus lateralis, obturator internus). In the test set (n = 40, 15 events), RSF achieved a C-index of 0.870 (95% CI, 0.764-0.957), outperforming the clinical Cox (0.804) and ridge Cox (0.853) model. Time-dependent AUCs of the RSF model were 0.984, 0.971, 0.913 and 0.850 at 12, 24, 36 and 48 months. RSF showed the best calibration, with the lowest IBS (0.076, 95% CI: 0.052-0.111), followed closely by the ridge Cox model (0.078) and the clinical Cox model (0.109). RSF stratified patients into low-risk, intermediate-risk and high-risk groups; no LOA events occurred in the low-risk group, with annual event rates were 7.2% and 34.6% in the intermediate- and high-risk groups (log-rank p = 0.0025). SHAP analysis identified exploratory thresholds (NSAA < 14.55, MRC% < 68.36%, rectus femoris FF > 32.30%, vastus lateralis FF > 58.00%, obturator internus FF > 7.43%). An interactive web application enables individualized risk prediction (https://westcndmd-timer-loa-risk-prediction-tool-8cygtrrbykvd9snmfcnhrp.streamlit.app/). CONCLUSIONS: A machine-learning-based RSF model integrating quantitative MRI FF metrics and clinical variables exhibited favourable predictive performance for time to LOA in DMD across short- and long-term horizons. It provided interpretable, individualized risk estimates and was deployed as an online tool to support clinical decision-making and trial enrichment.

Source: Journal of Cachexia Sarcopenia and Muscle

DOI: 10.1002/jcsm.70413