Machine learning-based preoperative prediction of IDH mutation status in adult gliomas using clinical features

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC-ROC 0.901 (internal validation), 0.849 (external validation)

• Primary Outcome: Diagnostic accuracy for IDH mutation status prediction

• Nnt Or Sensitivity: Sensitivity and specificity not reported

• Confidence Interval: Not reported

Clinical Application

Limited by lack of detail on required clinical features and implementation requirements Relevant to Australian neuro-oncology practice where IDH testing is standard; could complement existing molecular diagnostics if validated locally Adult glioma patients requiring preoperative IDH status assessment

Abstract

BACKGROUND: As isocitrate dehydrogenase (IDH) mutation status represents a critical prognostic factor in adult gliomas, there is a demand for a straightforward but effective predictive model to facilitate rapid preoperative diagnosis. METHODS: A cohort of 418 adult glioma patients from our institution were retrospectively enrolled and randomly partitioned into training (70%) and internal validation (30%) sets. Among 9 machine learning algorithms evaluated, AdaBoost demonstrated superior predictive performance based on receiver operating characteristic (ROC) analysis and was subsequently selected as the optimal model. Through feature importance ranking derived from the model, the top 8 most contributory features were identified and incorporated into the final streamlined prediction model. The model's generalizability was further assessed using an independent external validation cohort comprising 206 adult glioma cases. RESULTS: The AdaBoost model demonstrated robust discriminative performance, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.901 in the internal validation cohort and 0.849 in the independent external validation cohort. SHAP (SHapley Additive exPlanations) interpretation revealed age as the most influential predictive feature, contributing significantly to the model's decision-making process. CONCLUSIONS: This validated AdaBoost model offers a practical and interpretable solution for preoperative IDH status assessment, demonstrating both diagnostic reliability and biological plausibility through its feature importance profile.