A practical algorithm to predict postsurgical recurrence and progression of pituitary neuroendocrine tumours (PitNET)s.

OBJECTIVE

Pituitary neuroendocrine tumours (PitNET)s can be aggressive, thus presenting local invasion, postsurgical recurrence and/or resistance to treatment, responsible for significant morbidity. The study aimed at identifying prognostic factors of postsurgical outcome using data-driven classification of patients.

DESIGN

Retrospective observational study.

METHODS

Clinicopathological and radiological data of patients with PitNET treated via endoscopic endonasal surgery were collected. Tumour recurrence/progression and progression-free survival were assessed by classification tree analysis (CTA) and Kaplan-Meier curves, respectively. Histological subtype, cavernous/sphenoid sinus invasion, mitosis, Ki-67, p53, Trouillas' grading, degree of tumour exeresis and postsurgery disease activity were also evaluated.

RESULTS

A total of 1066 (466 gonadotroph, 287 somatotroph, 148 lactotroph, 157 corticotroph and 8 thyrotroph) tumours were included; 21.7% invaded the cavernous/sphenoid sinus. Based on Trouillas' classification, 64.3% were grade 1a, 14.2% 1b, 16.1% 2a, and 5.4% 2b; 18.3% had >2/10 HPF mitoses, 24.9% had Ki-67 ≥3%; 15.8% were positive for p53. Exeresis was radical in 81.2% of the cases. Median follow-up was 59.2 months. At last evaluation, 79.4% of the patients were cured; 20.6% had disease persistence, controlled by medical treatment in 18.3% of them. Disease recurrence/progression was recorded in 10.9% of the cases. CTA identified 5 distinct patient subgroups with different risk of disease recurrence/progression. Grade 2 of the Trouillas' grading, >2/10 HPF mitoses, Ki-67 ≥3%, p53 protein expression (P < .001), tumour invasion (P = .002) and ACTH-subtype (P = .003) were identified as risk factors of disease recurrence/progression.

CONCLUSIONS

The combined evaluation of Trouillas' grading, proliferation indexes and immunohistochemistry appears promising in the prediction of surgical outcome in PitNET.