<p>Compared to non-functional pituitary neuroendocrine tumors (NF-PitNETs), posterior pituitary tumors (PPTs) require more intraoperative protection of the pituitary stalk and hypothalamus, and their perioperative management is more complex than NF-PitNETs. However, they are difficult to be distinguished via magnetic resonance images (MRI) before operation. Based on clinical features and radiological signature extracted from MRI, this study aims to establish a model for distinguishing NF-PitNETs and PPTs. Preoperative MRI of 110 patients with NF-PitNETs and 55 patients with PPTs were retrospectively obtained. Patients were randomly assigned to the training (<i>n</i> = 110) and validation (<i>n</i> = 55) cohorts in a 2:1 ratio. The lest absolute shrinkage and selection operator (LASSO) algorithm was applied to develop a radiomic signature. Afterwards, an individualized predictive model (nomogram) incorporating radiomic signatures and predictive clinical features was developed. The nomogram’s performance was evaluated by calibration and decision curve analyses. Five features derived from contrast-enhanced images were selected using the LASSO algorithm. Based on the mentioned methods, the calculation formula of radiomic score was obtained. The constructed nomogram incorporating radiomic signature and predictive clinical features showed a good calibration and outperformed the clinical features for predicting NF-PitNETs and PPTs (area under the curve [AUC]: 0.937 vs. 0.595 in training cohort [<i>p</i> &lt; 0.001]; 0.907 vs. 0.782 in validation cohort [<i>p</i> = 0.03]). The decision curve shows that the individualized predictive model adds more benefit than clinical feature when the threshold probability ranges from 10 to 100%. Individualized predictive model provides a novel noninvasive imaging biomarker and could be conveniently used to distinguish NF-PitNETs and PPTs, which provides a significant reference for preoperative preparation and intraoperative decision-making.</p>