Research Article: Development and internal validation of a machine learning-based clinical decision support tool for surgical intervention in degenerative lumbar spondylolisthesis: a retrospective MRI study
Abstract:
Surgical decision-making for degenerative lumbar spondylolisthesis (DLS) is highly subjective and lacks quantitative, objective criteria. We sought to develop and validate a machine learning (ML) based clinical decision support tool using magnetic resonance imaging (MRI) features to identify appropriate candidates for surgical intervention.
We established a retrospective cohort of patients with single-level DLS. Patients were categorized into a surgical candidate group (underwent surgery with satisfactory outcomes) and a nonoperative group (stable or improved with conservative management for at least 6 months). Patients with poor surgical outcomes were excluded to ensure accurate ground truth labeling. A sensitivity analysis including these excluded patients was additionally performed to assess the robustness of the classification framework. Extracted features included clinical demographics and MRI parameters such as Pfirrmann grade, multifidus fatty infiltration, facet joint orientation, and degree of slippage. Four ML algorithms (Logistic Regression, LASSO, Random Forest, and XGBoost) were developed and evaluated using 5-fold cross-validation. Model performance was assessed via the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the best-performing model. Decision curve analysis suggested net clinical benefit across a range of threshold probabilities.
The XGBoost algorithm yielded the most accurate predictions, achieving an AUC of 0.86 (95% CI: 0.82–0.90) in the 5-fold cross-validation cohorts. SHAP analysis suggested that the degree of slippage, multifidus fatty infiltration, and Pfirrmann disc degeneration grade were the most influential MRI features associated with surgical necessity in this cohort. Decision curve analysis indicated that the XGBoost model provided a higher net clinical benefit across a range of threshold probabilities compared to default strategies. Sensitivity analysis including the 23 patients with poor surgical outcomes demonstrated a modest reduction in AUC (0.82, 95% CI: 0.77–0.87), confirming the overall robustness of the model.
In this single-center retrospective preliminary internal validation study, a machine learning model integrating routine MRI features and clinical parameters demonstrated good performance in distinguishing surgical candidates from patients amenable to conservative management. These findings are hypothesis-generating and require prospective, multi-center external validation before any consideration of clinical deployment.
Introduction:
Surgical decision-making for degenerative lumbar spondylolisthesis (DLS) is highly subjective and lacks quantitative, objective criteria. We sought to develop and validate a machine learning (ML) based clinical decision support tool using magnetic resonance imaging (MRI) features to identify appropriate candidates for surgical intervention.
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