Journal of cachexia, sarcopenia and muscle
Machine Learning Integrating Quantitative MRI and Clinical Features Predicts Loss of Ambulation in Duchenne Muscular Dystrophy
By Liu C, Song Y, Wan J and 8 others.
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.