Kyla Wright1, Matthew Lee2, Natalie Escobar1, Donato Pacione3, Matthew Young2, Girish Fatterpekar2, Nidhi Agrawal4. 1. NYU Grossman School of Medicine, NYU Langone Health, 550 1st Ave, New York, NY, 10016, USA. 2. Department of Radiology, NYU Langone Medical Center, 550 1st Avenue, New York, NY, 10016, USA. 3. Department of Neurosurgery, NYU Langone Medical Center, 1st Avenue, New York, NY, 10016, USA. 4. Division of Endocrinology, Diabetes and Metabolism, NYU Langone Medical Center/ Bellevue Hospital Center, 550 1st Avenue, New York, NY, 10016, USA. Nidhi.agrawal@nyulangone.org.
Abstract
PURPOSE: Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. METHODS: Adult patients with pathologically confirmed prolactinoma (n = 21) or NFA with hyperprolactinemia (n = 58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). RESULTS: Tumor volume and PRL positively correlated for prolactinoma (r = 0.4839, p = 0.0263) but not for NFA (r = 0.0421, p = 0.7536). PRL distinguished prolactinomas from NFAs with an AUC of 0.8892 (p < 0.0001) and optimal cut-off value of 62.45 ng/ml, yielding a sensitivity of 85.71% and specificity of 94.83%. The ratio of PRL to tumor volume had an AUC of 0.9647 (p < 0.0001) and optimal cut-off value of 21.62 (ng/ml)/cm3 with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (p < 0.0001). CONCLUSIONS: Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.
PURPOSE: Both prolactinomas and nonfunctioning adenomas (NFAs) can present with hyperprolactinemia. Distinguishing them is critical because prolactinomas are effectively managed with dopamine agonists, whereas compressive NFAs are treated surgically. Current guidelines rely only on serum prolactin (PRL) levels, which are neither sensitive nor specific enough. Recent studies suggest that accounting for tumor volume may improve diagnosis. The objective of this study is to investigate the diagnostic utility of PRL, tumor volume, and imaging features in differentiating prolactinoma and NFA. METHODS: Adult patients with pathologically confirmed prolactinoma (n = 21) or NFA with hyperprolactinemia (n = 58) between 2013 and 2020 were retrospectively identified. Diagnostic performance of clinical and imaging variables was analyzed using receiver-operating characteristic curves to calculate area under the curve (AUC). RESULTS:Tumor volume and PRL positively correlated for prolactinoma (r = 0.4839, p = 0.0263) but not for NFA (r = 0.0421, p = 0.7536). PRL distinguished prolactinomas from NFAs with an AUC of 0.8892 (p < 0.0001) and optimal cut-off value of 62.45 ng/ml, yielding a sensitivity of 85.71% and specificity of 94.83%. The ratio of PRL to tumor volume had an AUC of 0.9647 (p < 0.0001) and optimal cut-off value of 21.62 (ng/ml)/cm3 with sensitivity of 100% and specificity of 82.76%. Binary logistic regression found that PRL was a significant positive predictor of prolactinoma diagnosis, whereas tumor volume, presence of CSI not previously defined, and T2 hyperintensity were significant negative predictors. The regression model had an AUC of 0.9915 (p < 0.0001). CONCLUSIONS: Consideration of tumor volume improves differentiation between prolactinomas and NFAs, which in turn leads to effective management.
Authors: Anna Cho; Greisa Vila; Wolfgang Marik; Sigrid Klotz; Stefan Wolfsberger; Alexander Micko Journal: Front Endocrinol (Lausanne) Date: 2022-09-06 Impact factor: 6.055