Predicting pituitary neuroendocrine tumor risk based on clinical and MRI features nomogram: A multicenter study
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Objective The 2021 WHO classification redesignates pituitary adenomas as pituitary neuroendocrine tumors (PitNETs), incorporating transcription factor profiling for subtype stratification. Given the current diagnostic challenges in distinguishing high- and low-risk PitNETs, we investigated whether MRI features combined with clinical biomarkers could improve preoperative risk stratification. Methods This multicenter retrospective study analyzed 548 histopathologically confirmed PitNET cases (training set: n = 319; test set: n = 138 from Center 1; validation set: n = 91 from Center 2). Comprehensive clinical, endocrinological, and MRI parameters were evaluated through logistic regression to construct a predictive model. Diagnostic performance was quantified using area under the ROC curve (AUC), supplemented by calibration plots and decision curve analysis (DCA) to assess clinical utility. Results Significant intergroup differences (all p < 0.05) were observed between high- and low-risk PitNETs: patient age, maximal tumor diameter (p < 0.01), growth hormone (GH), prolactin (PRL), insulin-like growth factor-1 (IGF-1) levels, tumor margin irregularity, optic chiasm compression (p < 0.001), circumferential carotid encasement, and cavernous sinus invasion. Multivariate analysis identified age (OR = 1.04, 95%CI 1.02–1.07), tumor diameter (OR = 1.15, 95%CI 1.08–1.22), PRL (OR = 1.01, 95%CI 1.00-1.02), and IGF-1 (OR = 1.003, 95%CI 1.001–1.005) as independent predictors. The integrated model achieved an AUC of 0.803 (95%CI 0.703–0.903) on external validation set, with excellent calibration and favorable decision curve net benefit. Conclusions Nomogram by integrating clinical and MRI features can be used as a reliable tool to predict risk status in patients with PitNETs. After further external validation, this will help neurosurgeons make critical decisions regarding surgical or alternative treatment strategies for PitNETs.
