Utilization of SiPAP Classification in Prediction of Pituitary Adenoma Recurrence: The Ottawa Hospital Experience
Bibliographic record
Abstract
Introduction: Pituitary adenomas are a common skull base tumor that varies in size, often classified based on function and size, either micro- or macroadenoma. Patient clinical presentation depends on multiple factors, including excessive hormone secretion, the mass effect of large tumors, and tumor invasion to surrounding structures. With the advancement of diagnostic modalities, MRI is considered being the modality of choice to evaluate pituitary tumor features including parasellar extension. Endoscopic endonasal transsphenoidal (EETS) resection is the surgical standard of care for pituitary adenoma resection due to its superior visualization of the sella and surrounding anatomy. However, recurrence of the pituitary tumor following surgery has been reported widely. Yet, intuitively, adenoma size and involvement of parasellar structures should impact gross tumor resection (GTR) and recurrence. We evaluated a modified score using the SIPAP classification system, combining the suprasellar and parasellar extension scores of the pituitary tumor to determine its impact on adenoma recurrence. Study Design: This is a retrospective cohort, single-institutional study. Methods: Institutional REB approval was attained for a retrospective review of all EETS cases for pituitary tumor resection between November 2009 and October 2018. Queries of the hospital database were completed by medical records personnel to identify cases of pituitary tumor treated using the EETS approach. Patient characteristics, tumor type, endocrine data, and operation characteristics were then extracted from medical records pertaining to patient baseline characteristics. Preoperative MRI images were reviewed and the SIPAP classification applied to the pituitary tumors. Postoperative results were extracted for the duration of the follow-up period available for each patient. Within the SIPAP score, the suprasellar (S) and parasellar (P) scores are the most variable and believed to be the main drivers of surgical GTR. The suprasellar score and the highest parasellar scoring from both sides were numerically summed in a bilateral suprasellar and parasellar (SaP) score and combined to make 4 grades. Results: A total of 276 patients were identified, and 56.5% of the cohort was male. The mean age of the cohort was 54 years. During the study period, five different neurosurgeons performed EETS for patients with pituitary tumors. The mean of the length of follow-up was 32 months. Patient perioperative tumor grade according to SaP classification and recurrence rate in each grade were as follows: grade 1: 11%, grade 2: 10%, grade 3: 15%, and grade 4: 22%. The results followed a pattern of logarithmic curve. Conclusion: The SaP classification was demonstrated to be useful for determining the expected recurrence of a pituitary tumor following EETS, with the most advanced tumors demonstrating the highest rates of recurrence. Use of the SaP score may allow for more accurate preoperative counselling of patients with pituitary adenoma when considering recurrence requiring further surgery. Publication History Article published online: 12 February 2021 © 2021. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".