Radiomics for preoperative assessment of pituitary adenoma consistency with T2-weighted MRI: A multicenter study

Introduction: Pituitary adenoma (PA) consistency significantly influences the outcomes of endoscopic endonasal surgery. Radiomics represents a promising tool for objective and quantitative assessment using T2-weighted magnetic resonance imaging (MRI). Methods: A multicenter retrospective database was collected (2012–2023), including 394 patients with preoperative T2-weighted MRI and histologically confirmed PAs after endoscopic endonasal surgical removal. Tumor segmentation was performed manually on coronal T2-weighted images using ITK-SNAP software. Radiomic features were extracted with Pyradiomics. A 60:40 dataset split was used to train an Extra Trees (ET) classifier and recursive feature elimination was used to select features. Model performance was assessed using sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUR-ROC) curve metrics. Results: From 1,106 extracted radiomic features, 65 were identified as most predictive following variance and correlation filtering. The sensitivity, specificity and accuracy of ET classifier were 74%, 74%, and 63% (±10%), respectively. The AUC-ROC curve was 0.59. Conclusion: Despite its moderate accuracy and AUC-ROC curve, the ET model showed promise performance to predict preoperative PA consistency, underlying the power of radiomics-driven models in PAs surgical planning.