Granulation Patterns of Functional Corticotroph Tumors Correlate with Tumor Size, Proliferative Activity, T2 Intensity-to-White Matter Ratio, and Postsurgical Early Biochemical Remission
Bibliographic record
Abstract
Abstract Unlike somatotroph tumors, the data on correlates of tumor granulation patterns in functional TPIT lineage pituitary neuroendocrine tumors (corticotroph tumors) have been less uniformly documented in most clinical series. This study evaluated characteristics of 41 well-characterized functional corticotroph tumors consisting of 28 densely granulated corticotroph tumors (DGCTs) and 13 sparsely granulated corticotroph tumors (SGCTs) with respect to preoperative clinical and radiological findings, tumor proliferative activity (including mitotic count and Ki-67 labeling index), and postoperative early biochemical remission rates. The median (interquartile range (IQR)) tumor size was significantly larger in the SGCT group [16.00 (16.00) mm in SGCT vs 8.5 (9.75) mm in DGCT, p = 0.049]. T2-weighted signal intensity and T2 intensity (quantitative) did not yield statistical significance based on tumor granulation; however, the T2 intensity-to-white matter ratio was significantly higher in SGCTs ( p = 0.049). The median (IQR) Ki-67 labeling index was 2.00% (IQR 1.00%) in the DGCT group and 4.00% (IQR 7.00%) in the SGCT group ( p = 0.043). The mitotic count per 2 mm 2 was higher in the SGCT group ( p = 0.001). In the multivariate analysis, the sparse granulation pattern (SGCT) remained an independent predictor of a lower probability of early biochemical remission irrespective of the tumor size and proliferative activity ( p = 0.012). The current study further supports the impact of tumor granulation pattern as a biologic variable and warrants the detailed histological subtyping of functional corticotroph tumors as indicated in the WHO classification of pituitary neuroendocrine tumors. More importantly, the assessment of the quantitative T2 intensity-to-white matter ratio may serve as a preoperative radiological harbinger of SGCTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".