Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis

Background/objectives The consistency of pituitary adenoma (PA) significantly impacts surgical difficulty and the extent of resection. Machine learning (ML) and radiomics have emerged as quantitative tools to predict tumor firmness from MRI-derived features. This systematic review and meta-analysis aimed to synthesize the diagnostic performance of ML-based models for preoperative prediction of PA consistency. Methods PubMed, Embase, Scopus, and Web of Science were searched through September 2025. Studies developing or validating ML or deep learning (DL) models for predicting PA consistency were included. Pooled estimates of area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated with 95% confidence intervals (CIs). Results Nine studies with 1,621 patients were analyzed. Algorithms included Extra Trees (ET), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Logistic Regression (LR), Artificial Neural Network (ANN), and hybrid DL architectures. The pooled AUC was 0.92 (95% CI: 0.86–0.98), ACC 0.86 (95% CI: 0.79–0.92), SEN 0.80 (95% CI: 0.71–0.87), SPE 0.85 (95% CI: 0.80–0.89), and DOR 19.27 (95% CI: 10.27–36.17). Leave-one-out analyses confirmed robustness, and Egger’s tests indicated no significant publication bias. Conclusion ML-based models demonstrate excellent pooled diagnostic accuracy in predicting PA consistency preoperatively, underscoring their value for individualized surgical planning. Future multicenter studies with standardized imaging and external validation are needed to optimize clinical translation.