Role of prolactin/adenoma maximum diameter and prolactin/adenoma volume in the differential diagnosis of prolactinomas and other types of pituitary adenomas

A multicenter study of clinical outcomes and volumetric trends in suspected microprolactinomas

The diagnosis of pituitary microprolactinomas is often obscured by relatively low levels of elevated prolactin compared to macroprolactinomas. This may lead to varying patterns of medical therapy versus observation. We sought to correlate prolactin levels in suspected microprolactinomas with tumor volumes and clinical outcomes. This was a multicenter retrospective study of patients with pituitary microadenomas with baseline prolactin levels > 18ng/ml for males and > 30ng/ml for females. A linear-mixed model was used to depict changes in tumor volume over time. There were 65 patients with a mean tumor volume of 95.9mm3 and mean prolactin level of 59.4ng/ml. There were significantly higher prolactin levels in patients with tumors above the mean volume versus below (74.0 versus 53.4ng/ml, p = 0.027). 26 patients were observed, 31 were treated with anti-dopaminergic therapy, and 8 had surgery. There were significantly greater baseline prolactin levels for patients who were treated surgically (mean 86.4ng/ml) than those treated medically (mean 61.7 g/ml) or observed (mean 48.5ng/ml) (p = 0.02). Among the 26 patients who were surveilled, 13 patients demonstrated spontaneous tumor shrinkage, 12 remained stable, and 1 patient's tumor grew but was lost to follow-up. Linear mixed modeling demonstrated a statistically significant rate of tumor shrinkage over time of 3.67mm3/year (p = 0.03). When analyzing patients who were observed versus those requiring surgery after initially being surveilled, there were significantly greater baseline PRL/volume ratios in surgical patients versus those observed (8.1 ng/ml/mm3 versus 2.4 ng/ml/mm3, p = 0.025). Suspected microprolactinomas may demonstrate more convincingly elevated prolactin levels when measuring over 95.9mm3. Tumors with baseline prolactin levels over 50ng/ml may be more inclined to undergo medical treatment. In tumors with levels below 50ng/ml, it may be reasonable to undergo surveillance as these tumors tend to spontaneously shrink over time. In tumors that are surveilled, an elevated baseline PRL/volume ratio of > 8 ng/ml/mm3 may be indicate serial tumor growth that may necessitate medical and/or surgical intervention.

Voltage-dependent volume regulation controls epithelial cell extrusion and morphology

Epithelial cells work collectively to provide a protective barrier, yet also turn over rapidly by cell death and division. If the number of dying cells does not match those dividing, the barrier would vanish, or tumors can form. Mechanical forces and the stretch-activated ion channel (SAC) Piezo1 link both processes; stretch promotes cell division and crowding triggers cell death by initiating live cell extrusion1,2. However, it was not clear how particular cells within a crowded region are selected for extrusion. Here, we show that individual cells transiently shrink via water loss before they extrude. Artificially inducing cell shrinkage by increasing extracellular osmolarity is sufficient to induce cell extrusion. Pre-extrusion cell shrinkage requires the voltage-gated potassium channels Kv1.1 and Kv1.2 and the chloride channel SWELL1, upstream of Piezo1. Activation of these voltage-gated channels requires the mechano-sensitive Epithelial Sodium Channel, ENaC, acting as the earliest crowd-sensing step. Imaging with a voltage dye indicated that epithelial cells lose membrane potential as they become crowded and smaller, yet those selected for extrusion are markedly more depolarized than their neighbours. Loss of any of these channels in crowded conditions causes epithelial buckling, highlighting an important role for voltage and water regulation in controlling epithelial shape as well as extrusion. Thus, ENaC causes cells with similar membrane potentials to slowly shrink with compression but those with reduced membrane potentials to be eliminated by extrusion, suggesting a chief driver of cell death stems from insufficient energy to maintain cell membrane potential.

Diagnostic criteria of small sellar lesions with hyperprolactinemia: Prolactinoma or else

OBJECTIVE

To evaluate the combined predictive value of MRI criteria with the prolactin-volume-ratio (PVR) to differentiate prolactinoma from non-prolactinoma, in small sellar lesions with hyperprolactinemia.

METHODS

Retrospective analysis of 55 patients with sellar lesions of ≤15 mm diameter on MRI and hyperprolactinemia of ≤150 ng/mL, surgically treated between 2003 and 2020 at the Medical University of Vienna, with a conclusive histopathological report. Serum prolactin levels, extent of pituitary stalk deviation, size and volume of the lesion were assessed. The PVR was calculated by dividing the preoperative prolactin level by tumor volume.

RESULTS

Our study population consisted of 39 patients (71%) with a prolactin-producing pituitary adenoma (group A), while 16 patients (29%) had another type of sellar lesion (group B). Patients in group A were significantly younger (p=0.012), had significantly higher prolactin levels at diagnosis (p<0.001) as well as smaller tumor volume (p=0.036) and lower degree of pituitary stalk deviation (p=0.009). The median PVR was significantly higher in group A (243 ng/mL per cm³) than in group B (83 ng/mL per cm³; p=0.002). Furthermore, the regression operating characteristics analysis revealed a PVR >100 ng/mL per cm³ to be predictive for distinguishing prolactin-producing lesions from other small sellar lesions.

CONCLUSION

In patients with small sellar lesions, Prolactin-Volume-Ratios >100 represents a possible predictive marker for the diagnosis of prolactin-producing pituitary adenomas.

Metabolic effects of prolactin and the role of dopamine agonists: A review

Prolactin is a polypeptide hormone that is well known for its role in reproductive physiology. Recent studies highlight its role in neurohormonal appetite regulation and metabolism. Elevated prolactin levels are widely associated with worsening metabolic disease, but it appears that low prolactin levels could also be metabolically unfavorable. This review discusses the pathophysiology of prolactin related metabolic changes, and the less commonly recognized effects of prolactin on adipose tissue, pancreas, liver, and small bowel. Furthermore, the effect of dopamine agonists on the metabolic profiles of patients with hyperprolactinemia are discussed as well.

Tumor volume improves preoperative differentiation of prolactinomas and nonfunctioning pituitary adenomas

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.