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Multicentric Prospective Validation of the Zurich Pituitary Score

Completed
Conditions
Pituitary Adenoma
Registration Number
NCT04076046
Lead Sponsor
University of Zurich
Brief Summary

Predictive analytics for GTR, EOR and RV are useful in surgical decision-making, particularly whenever there is no unequivocal indication for surgery. Several factors have been shown to have a role in predicting GTR. Among these, the Knosp classification has proven over the years to be a good predictor of GTR. The score is based on the lateral extension of the adenoma in relation the the intracranial bedding of the internal carotid artery. However, recent literature has demonstrated that the Knosp classification suffers from relatively poor interrater agreement. Moreover the classification was conceived in an era when endoscopic techniques were not available: nowadays endoscopic technique allows visualization and possibly also reaching portions of adenoma which at the time when the Knosp classification was introduced were simply not possible. Lastly, the efficacy of the Knosp's score in predicting also EOR and RV has never been tested.

Recently a new score - the Zurich Pituitary Score (ZPS) has been proposed at the University Hospital of Zürich (USZ). The score has proved in the examined series to be more powerful than the Knosp classification in predicting GTR, EOR and RV. A good interrater agreement was also demonstrated. The score however, has been validated only in a monocentric setting with a limited number of patients. The aim of this study is to assess the (1) predictive ability of the ZPS for GTR, EOR, and RV, and (2) the inter-rater agreement of the ZPS in an external validation study.

Detailed Description

In transsphenoidal surgery (TSS) for pituitary adenoma (PA), gross total resection (GTR) is often the target since it has been shown to relevantly influence long term patient outcome. This applies both to functioning adenomas (FA) as well as to non-functioning adenomas (NFPA). Mounting evidence shows that in cases where GTR is not achievable, maximizing extent of resection (EOR) is still highly desirable, particularly in case of FA, with the aim of obtaining the smallest possible residual volume (RV).

Predictive analytics for GTR, EOR and RV are useful in surgical decision-making, particularly whenever there is no unequivocal indication for surgery. Several factors have been shown to have a role in predicting GTR. Among these, the Knosp classification has proven over the years to be a good predictor of GTR. The score is based on the lateral extension of the adenoma in relation the the intracranial bedding of the internal carotid artery. However, recent literature has demonstrated that the Knosp classification suffers from relatively poor interrater agreement. Moreover the classification was conceived in an era when endoscopic techniques were not available: nowadays endoscopic technique allows visualization and possibly also reaching portions of adenoma which at the time when the Knosp classification was introduced were simply not possible. Lastly, the efficacy of the Knosp's score in predicting also EOR and RV has never been tested.

Recently a new score - the Zurich Pituitary Score (ZPS) has been proposed at the University Hospital of Zürich (USZ). The score has proved in the examined series to be more powerful than the Knosp classification in predicting GTR, EOR and RV. A good interrater agreement was also demonstrated. The score however, has been validated only in a monocentric setting with a limited number of patients. The aim of this study is to assess the (1) predictive ability of the ZPS for GTR, EOR, and RV, and (2) the inter-rater agreement of the ZPS in an external validation study.

Recruitment & Eligibility

Status
COMPLETED
Sex
All
Target Recruitment
408
Inclusion Criteria
  • Patients harboring suspected pituitary adenoma.
  • Patients undergoing endoscopic transsphenoidal surgery
  • Preoperative MRI including at least one volumetric sequence (i.e. MPRAGE or SPGR) with gadolinium with possibility for coronal reconstructions
  • Postoperative MRI (2-4 months postoperative) including at least one volumetric sequence (i.e. MPRAGE or SPGR) with Gadolinium with possibility for coronal reconstructions
Exclusion Criteria
  • Patients undergoing combined or transcranial operations
  • Patients for whom a biopsy only is planned
  • Patients by whom histology does not confirm the diagnosis of pituitary adenoma

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Extent of Resection2 to 4 months postoperatively

Percentage of removed adenoma volume on magnetic resonance imaging

Residual Adenoma Volume2 to 4 months postoperatively

Percentage of original adenoma volume still visible at the postoperative magnetic resonance imaging

Gross Total Resection2 to 4 months postoperatively

Gross Total Resection on magnetic resonance imaging

Zurich Pituitary ScoreOn preoperative MRI

Four-tier score as follows:

Grade Criteria I R \< 0.75 II 0.75 \< R \< 1.25 III 1.25 \< R IV Encasement of the intracavernous ICA

R = ratio of maximum horizontal tumor diameter divided by the minimum intercarotid distance at the intracavernous horizontal C4 segment of the ICA.

Secondary Outcome Measures
NameTimeMethod

Trial Locations

Locations (10)

Department of Neurosurgery

🇨🇭

Zürich, Switzerland

Universita' degli Studi Federico II

🇮🇹

Napoli, Italy

Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

🇺🇸

New York, New York, United States

Department of Neurosurgery, Kepler Universitäts Klinikum

🇦🇹

Linz, Austria

Azienda Ospedaliera Universitaria Careggi

🇮🇹

Firenze, Italy

Department of Neurosurgery and Neurooncology, First Faculty of Medicine, Charles University and Military University Hospital

🇨🇿

Praha, Czechia

Toronto Western Hospital, University of Toronto

🇨🇦

Toronto, Canada

IRCCS Istituto delle Scienze Neurologiche di Bologna

🇮🇹

Bologna, Italy

Reparto di Neurochirurgia, Ospedale di Circolo e Fondazione Macchi, Universita' dell'Insubria

🇮🇹

Varese, Italy

CHUV

🇨🇭

Lausanne, Switzerland

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