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

Federica Guaraldi; Matteo Zoli; Alberto Righi; Dino Gibertoni; Valentino Marino Picciola; Marco Faustini-Fustini; Luca Morandi; Antonella Bacci; Ernesto Pasquini; Diego Mazzatenta; Sofia Asioli

doi:10.1111/cen.14197

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

Clin Endocrinol (Oxf). 2020 Jul;93(1):36-43. doi: 10.1111/cen.14197. Epub 2020 May 5.

Authors

Federica Guaraldi^{1

2}, Matteo Zoli^{1

2}, Alberto Righi³, Dino Gibertoni⁴, Valentino Marino Picciola^{1

2}, Marco Faustini-Fustini¹, Luca Morandi², Antonella Bacci⁵, Ernesto Pasquini⁶, Diego Mazzatenta^{1

2}, Sofia Asioli^{1

7}

Affiliations

¹ IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
² Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.
³ Department of Pathology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
⁴ Department of Biomedical and Neuromotor Sciences (DIBINEM), Unit of Hygiene and Biostatistics, University of Bologna, Bologna, Italy.
⁵ Division of Neuroradiology, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
⁶ ENT Division, Bellaria Hospital, Bologna, Italy.
⁷ Department of Biomedical and Neuromotor Sciences (DIBINEM), Section of Anatomic Pathology 'M. Malpighi' at Bellaria Hospital, University of Bologna, Bologna, Italy.

PMID: 32306401
DOI: 10.1111/cen.14197

Abstract

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.

Keywords: classification tree analysis; outcome; pituitary adenoma; pituitary tumour; progression.

Publication types

Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Humans
Neoplasm Recurrence, Local
Neuroendocrine Tumors* / surgery
Pituitary Gland
Pituitary Neoplasms* / surgery
Retrospective Studies