Identifying Subgroups with Rapid Tumor Growth Rate in Adult Pituitary Neuroendocrine Tumors: A Comprehensive Analysis of Clinical and Imaging Features
Year: 2024
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: To comprehensively investigate the clinical and imaging features associated with the tumor growth rate (TGR) of pituitary neuroendocrine tumors (PitNETs). METHODS: The 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. RESULTS: The 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. CONCLUSIONS: The 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.
Source: World Neurosurgery
Authors: Zhe Zhang, Peng Li, Xiaojie Yang, Jie Yin, Junhua He, Yanan Hu, Pinan Liu
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,920
Awards: G1127362942, G5916224071
OpenAlex ID: W4404873463
