Endocrine Abstracts

In the current era of multi-modal imaging being performed for various pathologies, incidental non-functioning pituitary microadenomas (NFPAs) are being increasingly identified. The available literature and guidance is limited on the long-term surveillance and monitoring and hence there is a wide variation in clinical practice across the UK. We conducted a retrospective study in our institution to compare the outcome in our cohort to the current literature.

Method

Keyphrase “pituitary adenoma” was used to select 1700 patients over a 10-year period (2010–20) from pituitary MDM, radiology database and electronic patient records. Macroadenomas, functioning tumours, Rathke’s cleft cysts, MEN syndrome and patients with < 2 pituitary screening (imaging and biochemistry) were excluded. Retrospective analysis was conducted on 43 patients with a mean age of 51 ±13 years (32F and 11M). NFPA diameter ranges were 1 – 5 mm in 24 (56%) and 6 - £10 mm in 19 patients (44%).

Results

Mean follow up period was 28.5 months (range 3 – 96) equating to 102 patient years. Although 20% of cases progressed in size (0.72 mm/year), overall net growth was negative (- 0.13 mm/year) as 80% regressed or remained unchanged. The maximum dimension reached during surveillance was 11mm in 1 NFPA from a baseline of 10mm.

Surveillance summary

1 – 10 mm: Regression in 30%, unchanged 50% and progression in 20%. 1 – 5 mm: Regression in 25%, unchanged in 60% and progression in 15%. 6 – 10 mm: regression in 30%, unchanged in 40% and progression in 30%.

Conclusion

Compared to the largest meta-analysis of 14 multi-centre observational studies that consisted of 229 NFPAs, our single centre case series of 43 NFPAs demonstrated 20% (vs 10%) progression, 30% (vs 7%) regression and 50% (vs 83%) unchanged with stable size. None of the NFPAs in our series came into contact with the optic chiasm and all the patients remained eupituitary throughout. Our study further reaffirms the benign course of NFPA if £10 mm, however, further up to date multi-centre data collation is essential for predictive modelling based on size and age at presentation and standardisation of follow up and discharge protocols.