Growth Patterns of Pituitary Adenomas and Histopathological Correlates
Bibliographic record
Abstract
CONTEXT: The factors associated with pituitary adenoma (PA) growth rate remain unclear. OBJECTIVE: The objective of the study was to establish whether the preoperative growth and extension pattern of PA can predict postoperative growth rate and recurrence in addition to whether the PA growth rate correlates with proliferation and growth factor expression. PATIENTS: One hundred fifty-three consecutive patients who underwent surgery for pituitary adenoma from 1999 to 2011 at Toronto Western Hospital were identified. MAIN OUTCOME MEASURES: The PA growth rate was measured both pre- and postoperatively, and its association with patient demographics, magnetic resonance imaging, and histolopathological parameters was determined. RESULTS: The preoperative growth rate was associated with age (P = .0001), suprasellar growth (P = .003), the presence of a cyst/hemorrhage (P = .004), the mindbomb homolog-1 (P = .005), fibroblast growth factor receptor-4 positivity (P = .047), and p27 negativity (P = .007). After surgery, there were 34.6% residual volumes, which were associated with older age (P = .038) and also with growth patterns including anterior, posterior, suprasellar, and cavernous sinus extension (P = .001); 43.3% of these residuals grew and postoperative growth rate was calculated. Pre- and postoperative growth rates were correlated (r = 0.497, P = .026). Postoperative growth rate was associated with age (P = .015) and gender (P = .017). CONCLUSIONS: Our data suggest that the growth rate of PAs are influenced by various patient- and tumor-specific characteristics including the age and sex of the patient, the specific subtype of PA, its hormonal activity, its immunohistochemical profile including the mindbomb homolog 1 labeling index status, and its preponderance for different growth directions relative to the pituitary fossa. Furthermore, the pre- and postoperative PA growth rates were correlated, suggesting that postoperative PA growth rates can be predicted, in part, by preoperative growth rates, thus better informing postoperative outcome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".