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Investigation of the growth patterns of non-functioning pituitary macroadenomas using volumetric assessments on serial MRI investigations

Jaco Pieterse, Coert S. de Vries, Susanna F. Otto

Abstract


Background: Benign non-functioning pituitary macroadenomas (NFMA) often cause mass effect on the optic chiasm necessitating transsphenoidal surgery to prevent blindness. However, surgery is complicated and there is a high tumour recurrence rate. Currently, very little is known about the natural (and residual post-surgical) growth patterns of these NFMA. Conflicting data describe decreased growth to exponential growth over various time periods. Due to lack of information on growth dynamics of these NFMA, suitable follow-up imaging protocols have not been described to date.
Objective: To determine if NFMA grow or stay quiescent over a time period using serial MRI investigations and a stereological method to determine tumour volume. In addition, to evaluate if NFMA adhere to a certain growth pattern or grow at random.
Method: Thirteen patients with NFMA had serial MRI investigations over a 73-month period at the Universitas Academic Hospital. Six of the selected patients had undergone previous surgery, while seven patients had received no medical or surgical intervention. By using a stereological method, tumour volumes were calculated and plotted over time to demonstrate growth curves. The data were then fitted to tumour growth models already described in literature in order to obtain the best fit by calculating the r2 value.
Results: Positive tumour growth was demonstrated in all cases. Tumour growth patterns of nine patients best fitted the exponential growth curve while the growth patterns of three patients best fitted the logistic growth curve. The remaining patient demonstrated a linear growth pattern.
Conclusion: A specific growth model best described tumour growth observed in non-surgical and surgical cases. If follow-up imaging confirms positive growth, future growth can be predicted by extrapolation. This information can then be used to determine the relevant follow-up-imaging interval in each individual patient.




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