Clinical integration and application of the 2022 WHO pituitary tumor classification
Bibliographic record
Abstract
The pituitary is a complex endocrine organ that gives rise to adenohypophysial epithelial neuroendocrine tumors, to hypothalamic neuroendocrine neuronal neoplasms, to nonendocrine glial-related pituicytomas, and to Rathke's pouch-derived craniopharyngiomas and pituitary blastomas. The past 30 years have seen significant advances in the classification of these tumors based on cell lineages, molecular alterations, and biomarker features that distinguish tumor types and their subtypes. Adenohypophysial cells give rise to pituitary neuroendocrine tumors (PitNETs) that usually follow 1 of 3 cell differentiation pathways. Pituicytomas have multiple morphologic variants and hypothalamic neuronal tumors can be gangliocytomas composed of magnocellular neurons or neurocytomas composed of small neurocytes. Craniopharyngiomas have 2 distinct families, papillary and adamantinomatous, with different alterations in signaling pathways. Pituitary blastomas are primitive tumors composed of an admixture of immature elements of Rathke's pouch with immature but differentiated hormone-secreting adenohypophysial cells. These various tumors are associated with specific clinical manifestations, creating a complex clinico-radiologic-pathologic classification that has both important clinical distinctions but also predictive therapeutic value. The syndromic disease has allowed the identification of genes that may be implicated in the development of these tumors, but the majority are sporadic tumors that, with a few exceptions, have no recurrent genetic alterations. The structure-function correlations that have been elucidated are critical to allow subclassification of clinical disorders that will pave the way for further advances in understanding genetic predisposition and environmental factors that alter epigenetic profiles of pituitary cells.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".