title: Growth modelling of non-functioning pituitary adenomas in patients referred for surgery.
abstract: OBJECTIVE
  Recent observational studies have established progression and recurrence rates of pituitary adenomas. However, it is still unknown how individual pituitary adenomas grow over years and whether growth kinetics follow a distinct growth model. The objective of this study was to define a growth model for non-functioning pituitary adenomas.
  
  
  METHODS
  Fifteen patients who had five or more serial high-quality examinations with magnetic resonance images or computerized tomography scans were identified among 216 patients with non-functioning pituitary adenomas. Tumour volumes were assessed using a stereological method based on the Cavalieri principle. Tumour growth during the observation period was analysed and different growth models were fitted to the data.
  
  
  RESULTS
  Fifteen pituitary adenomas (12 recurrent tumours and 3 newly diagnosed tumours) were longitudinally observed during a median observation period of 7.4 years (range: 2.3-11.9 years). Growth kinetics could be described either by an exponential growth model (nine patients) or by a logistic model (five patients) with initial exponential growth followed by deceleration of growth. One tumour remained unchanged in size during the observation period. None of the adenomas showed accelerated growth during the observation period. Overall, the linear growth model was not suitable to describe the growth kinetics of non-functioning pituitary adenomas.
  
  
  CONCLUSIONS
  Our study shows that growth of pituitary adenomas can be described by distinct growth models. Knowledge of growth dynamics has implications for clinical practice and helps to adjust scanning protocols for follow-up investigations.
authors.affiliation: Department of Neurosurgery, University of Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany. juergen.honegger@med.uni-tuebingen.de
authors.forename: Juergen
authors.initials: J
authors.lastname: Honegger
authors.forename: Sanna
authors.initials: S
authors.lastname: Zimmermann
authors.forename: Tsambika
authors.initials: T
authors.lastname: Psaras
authors.forename: Manfred
authors.initials: M
authors.lastname: Petrick
authors.forename: Michel
authors.initials: M
authors.lastname: Mittelbronn
authors.forename: Ulrike
authors.initials: U
authors.lastname: Ernemann
authors.forename: Martin
authors.initials: M
authors.lastname: Reincke
authors.forename: Klaus
authors.initials: K
authors.lastname: Dietz
mesh_terms: D000236:Adenoma / Q000473:pathology* / Q000503:physiopathology* / Q000601:surgery; D000328:Adult; D000368:Aged; D018450:Disease Progression; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D010911:Pituitary Neoplasms / Q000473:pathology* / Q000503:physiopathology* / Q000601:surgery; D011237:Predictive Value of Tests; D012017:Referral and Consultation; D014057:Tomography, X-Ray Computed