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Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas

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A Correction to this article was published on 05 July 2021

A Correction to this article was published on 07 June 2021

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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 cavernous sinus invasion, 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.

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References

  1. A.F. Daly, M. Rixhon, C. Adam, A. Dempegioti, M.A. Tichomirowa, A. Beckers, High prevalence of pituitary adenomas: a cross-sectional study in the province of Liege, Belgium. J. Clin. Endocrinol. Metab. 91(12), 4769–4775 (2006)

    Article  CAS  Google Scholar 

  2. J.A. Romijn, Hyperprolactinemia and prolactinoma. Handb. Clin. Neurol. 124, 185–195 (2014)

    Article  Google Scholar 

  3. M.E. Molitch, Disorders of prolactin secretion. Endocrinol. Metab. Clin. North Am. 30(3), 585–610 (2001)

    Article  CAS  Google Scholar 

  4. A. Glezer, M.D. Bronstein, Prolactinomas. Endocrinol. Metab. Clin. North Am. 44(1), 71–78 (2015)

    Article  Google Scholar 

  5. D.L. Penn, W.T. Burke, E.R. Laws, Management of non-functioning pituitary adenomas: surgery. Pituitary 21(2), 145–153 (2018)

    Article  Google Scholar 

  6. S. Melmed, F.F. Casanueva, A.R. Hoffman et al. Diagnosis and treatment of hyperprolactinemia: an Endocrine Society clinical practice guideline. J. Clin. Endocrinol. Metab. 96(2), 273–288 (2011)

    Article  CAS  Google Scholar 

  7. L. Vilar, C.F. Vilar, R. Lyra, M.D.C. Freitas, Pitfalls in the diagnostic evaluation of hyperprolactinemia. Neuroendocrinology 109(1), 7–19 (2019)

    Article  CAS  Google Scholar 

  8. I. Samperi, K. Lithgow, N. Karavitaki, Hyperprolactinaemia. J. Clin. Med 8, 12 (2019)

    Article  Google Scholar 

  9. Y. Huang, C. Ding, F. Zhang, D. Xiao, L. Zhao, S. Wang, Role of prolactin/adenoma maximum diameter and prolactin/adenoma volume in the differential diagnosis of prolactinomas and other types of pituitary adenomas. Oncol. Lett. 15(2), 2010–2016 (2018)

    PubMed  Google Scholar 

  10. Burke WT, Penn DL, Castlen JP, et al. Prolactinomas and nonfunctioning adenomas: preoperative diagnosis of tumor type using serum prolactin and tumor size. J Neurosurg. 1–8 (2019)

  11. L. Kotsis, Z. Krisár, J. Imre, M. Csikos, Complications of oesophagoplasty with isoperistaltic transverse colon. Scand. J. Thorac. Cardiovasc. Surg. 17(3), 317–321 (1983)

    Article  CAS  Google Scholar 

  12. E. Knosp, E. Steiner, K. Kitz, C. Matula, Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery 33(4), 610–617 (1993). discussion 617–618

    CAS  PubMed  Google Scholar 

  13. A.S. Micko, A. Wohrer, S. Wolfsberger, E. Knosp, Invasion of the cavernous sinus space in pituitary adenomas: endoscopic verification and its correlation with an MRI-based classification. J. Neurosurg. 122(4), 803–811 (2015)

    Article  Google Scholar 

  14. F. Zhang, Y. Huang, C. Ding, G. Huang, S. Wang, The prevalence of hyperprolactinemia in non-functioning pituitary macroadenomas. Int J. Clin. Exp. Med 8(10), 18990–18997 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  15. M.V. Smith, E.R. Laws Jr, Magnetic resonance imaging measurements of pituitary stalk compression and deviation in patients with nonprolactin-secreting intrasellar and parasellar tumors: lack of correlation with serum prolactin levels. Neurosurgery 34(5), 834–839 (1994). discussion 839

    CAS  PubMed  Google Scholar 

  16. N. Karavitaki, G. Thanabalasingham, H.C. Shore et al. Do the limits of serum prolactin in disconnection hyperprolactinaemia need re-definition? A study of 226 patients with histologically verified non-functioning pituitary macroadenoma. Clin. Endocrinol. 65(4), 524–529 (2006)

    Article  CAS  Google Scholar 

  17. T. Kawaguchi, Y. Ogawa, T. Tominaga, Diagnostic pitfalls of hyperprolactinemia: the importance of sequential pituitary imaging. BMC Res Notes 7, 555 (2014)

    Article  Google Scholar 

  18. A. Hagiwara, Y. Inoue, K. Wakasa, T. Haba, T. Tashiro, T. Miyamoto, Comparison of growth hormone-producing and non-growth hormone-producing pituitary adenomas: imaging characteristics and pathologic correlation. Radiology 228(2), 533–538 (2003)

    Article  Google Scholar 

  19. M. Gruppetta, J. Vassallo, Epidemiology and radiological geometric assessment of pituitary macroadenomas: population-based study. Clin. Endocrinol. 85(2), 223–231 (2016)

    Article  CAS  Google Scholar 

  20. E.V. Varlamov, J.M. Hinojosa-Amaya, M. Fleseriu, Magnetic resonance imaging in the management of prolactinomas; a review of the evidence. Pituitary 23(1), 16–26 (2020)

    Article  CAS  Google Scholar 

  21. J. Kreutz, L. Vroonen, F. Cattin et al. Intensity of prolactinoma on T2-weighted magnetic resonance imaging: towards another gender difference. Neuroradiology 57(7), 679–684 (2015)

    Article  Google Scholar 

  22. S.C. Dogansen, G.Y. Yalin, S. Tanrikulu et al. Clinicopathological significance of baseline T2-weighted signal intensity in functional pituitary adenomas. Pituitary 21(4), 347–354 (2018)

    Article  Google Scholar 

  23. M.C. Burlacu, D. Maiter, T. Duprez, E. Delgrange, T2-weighted magnetic resonance imaging characterization of prolactinomas and association with their response to dopamine agonists. Endocrine 63(2), 323–331 (2019)

    Article  CAS  Google Scholar 

  24. M.B.S. Lopes, The 2017 World Health Organization classification of tumors of the pituitary gland: a summary. Acta Neuropathol. 134(4), 521–535 (2017)

    Article  CAS  Google Scholar 

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Author notes

  1. These authors contributed equally: Kyla Wright, Matthew Lee

Authors and Affiliations

  1. NYU Grossman School of Medicine, NYU Langone Health, 550 1st Ave, New York, NY, 10016, USA

    Kyla Wright & Natalie Escobar

  2. Department of Radiology, NYU Langone Medical Center, 550 1st Avenue, New York, NY, 10016, USA

    Matthew Lee, Matthew Young & Girish Fatterpekar

  3. Department of Neurosurgery, NYU Langone Medical Center, 1st Avenue, New York, NY, 10016, USA

    Donato Pacione

  4. Division of Endocrinology, Diabetes and Metabolism, NYU Langone Medical Center/ Bellevue Hospital Center, 550 1st Avenue, New York, NY, 10016, USA

    Nidhi Agrawal

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  1. Kyla Wright
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Correspondence to Nidhi Agrawal.

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Conflict of interest

Kyla Wright, Dr. Matthew Lee, Natalie Escobar, Dr. Donato Pacione, Dr. Girish Fatterpekar, and Dr. Matthew Young have nothing to disclose. Dr. Nidhi Agrawal is on the advisory board for Corcept Therapeutics and is a principal investigator for institution-directed research grants (Chiasm Acromegaly Registry).

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This study was approved by the institutional review board and carried out in accordance with the approved guidelines.

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Wright, K., Lee, M., Escobar, N. et al. Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas. Endocrine 74, 138–145 (2021). https://doi.org/10.1007/s12020-021-02744-8

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