• Zhe Zhang, Peng Li, Xiaojie Yang, Jie Yin, Junhua He, Yanan Hu, and Pinan Liu.
• Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing, China.
• World Neurosurg. 2024 Dec 19: 123520123520.

ObjectiveTo comprehensively investigate the clinical and imaging features associated with the tumor growth rate (TGR) of pituitary neuroendocrine tumors (PitNETs).MethodsThe tumor volume was assessed using magnetic resonance imaging. The potential growth-related parameters were compared among different TGR subgroups. Logistic regression analysis and receiver operating characteristic curves were used to identify risk factors and evaluate their diagnostic accuracy for rapid TGR, respectively.ResultsThe study included 81 patients with PitNETs who met the inclusion criteria. Receiver operating characteristic curves were used to determine the optimal cut-off values for age and tumor volume at initial diagnosis. The factors significantly associated with rapid TGR were age <55 years, T2 heterogeneity, and Knosp grade ≥3 (P < 0.05). No significant differences were found among other clinical and imaging subgroups. Multivariate regression analysis confirmed that these factors increased the risk of rapid TGR (P < 0.05). The area under the curve for predicting rapid TGR using age <55 years, T2 heterogeneity, Knosp grade ≥3, and a combined model of these factors were 0.677 (95% confidence interval [CI], 0.564-0.777), 0.705 (95% CI, 0.593-0.801), 0.680 (95% CI, 0.567-0.780), and 0.834 (95% CI, 0.735-0908), respectively. Additionally, the expression of cell lineage-specific transcription factors and Ki-67 exhibited a significant correlation with age <55 years and T2 heterogeneity; however, no association was observed with Knosp grade.ConclusionsThe TGR of PitNETs is associated with age, T2 heterogeneity, and Knosp grade. Integrating these factors improves the accuracy of prediction for TGR. Therefore, understanding the TGR in PitNETs can provide valuable evidence for tailoring individualized treatment strategies for patients.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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