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Published on: January 17, 2018
Role of Tumor Volume and Prolactin Ratios in Differentiating Prolactinomas and Predicting Response to Cabergoline
Manuel Ramón García-Sáenz1, Jesús Iván Castro-Murillo1, Moisés Mercado2
1Department of Endocrinology, Hospital de Especialidades Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico.
Introduction:
Predicting resistance to cabergoline in prolactinomas remains a challenge. While prolactin (PRL)-based ratios help differentiate prolactinomas from non-functioning pituitary adenomas (NFPAs), their role in forecasting therapeutic response is unclear.
Objectives:
To assess the usefulness of PRL-based ratios and tumor volume in distinguishing between prolactinomas and NFPAs, as well as in predicting cabergoline resistance.
Methods:
We conducted a retrospective study of 86 patients (46 NFPAs, 40 prolactinomas). Resistance was reclassified according to the updated 2023 Pituitary Society criteria, and PRL, tumor volume (software-measured and ellipsoid-calculated), and PRL-derived ratios (PRL/maximum diameter [MD)], PRL/volume [V)]) were evaluated using ROC analysis.
Results:
PRL and PRL/MD ratio excelled at differentiating prolactinomas from NFPAs (AUC = 0.999). Calculated tumor volume demonstrated moderate discriminative capacity for predicting cabergoline resistance (AUC = 0.668, p = 0.084), with an exploratory cutoff of 65.64 cm3 yielding a sensitivity of 64.3% and a specificity of 69.2%. PRL levels and PRL-based ratios were not predictive of resistance.
Discussion:
Calculated tumor volume showed exploratory predictive value but did not reach statistical significance. Importantly, baseline structural parameters should not be interpreted as substitutes for the early biochemical and radiological response at three to six months, as these responses remain the most reliable predictors of long-term responsiveness to dopamine agonists. Rather, tumor volume may represent a preliminary pre-treatment structural risk marker that requires further prospective validation.

