Comprehensive Comparison of Visual Outcomes in Macro and Giant Prolactinoma Cohorts Managed With Medical Versus Surgical Treatment

BACKGROUND AND OBJECTIVES: Macro (>1 cm) and giant prolactinomas (>4 cm) are large prolactin-secreting tumors associated with chiasmal compression and vision loss. Although most patients are treated medically, select cases with severe acute vision loss may be considered for urgent surgical treatment. The purpose of this study was to determine whether first-line dopamine agonist therapy or surgery offered superior visual outcomes for patients with macro and giant prolactinomas using retinal nerve fiber layer (RNFL) thickness and logarithm of minimum angle of resolution (logMAR) visual acuity (VA). METHODS: A retrospective review was performed on all patients at our institution who received first-line medical or surgical treatment for macro or giant prolactinomas between 2000 and 2022. Recurrent cases and those without a baseline neuro-ophthalmological examination were excluded. Vision status, RNFL thickness, and logMAR VA were collected at baseline and at 3 months. Linear mixed models were generated to investigate the interaction between initial treatment modality and change in RNFL thickness and logMAR over time. Pearson correlation and Wilcoxon rank sum tests were performed when appropriate. RESULTS: Forty-two patients were included, of whom 29 (69.0%) received first-line medical therapy and 13 (31%) received first-line surgery. Nineteen (65.5%) medical and 10 (76.9%) surgical patients had chiasmal compression at baseline. Change in RNFL thickness over time was independent of treatment modality ( P = .990). Treatment modality did not influence change in logMAR VA over time ( P = .891). When incorporating chiasmal compression into the linear mixed model, change in RNFL thickness and logMAR VA over time remained independent of treatment. CONCLUSION: In this patient cohort, available data showed that visual outcomes did not differ significantly by treatment modality. Further studies with larger sample sizes may be able to elucidate additional insights but treatment selection must continue to reflect individual patient characteristics at presentation.