Hematologic Malignancies: Two Cases of a Rare Cause of Hypopituitarism
Bibliographic record
Abstract
Hematologic malignancies are rare causes of sellar masses and hypopituitarism. We report 2 cases of hypopituitarism due to sellar masses from hematologic malignancies. The first patient was found to have hypopituitarism but initial non-gadolinium-enhanced magnetic resonance imaging (MRI) sella did not demonstrate a mass. Subsequent gadolinium-enhanced MRI and transsphenoidal biopsy confirmed a diagnosis of intravascular lymphoma. Treatment with systemic chemotherapy resulted in resolution of abnormalities on MRI. The second patient had a known diagnosis of chronic lymphocytic leukemia, and sellar involvement contributing to hypopituitarism was confirmed on biopsy. Treatment with ibrutinib, acalabrutinib, and stereotactic radiosurgery resulted in resolution of abnormalities on MRI. Both patients were treated with hormone replacement for hypopituitarism. These cases highlight that hematologic malignancies should be suspected as causes of sellar masses/hypopituitarism in patients with concurrent symptoms atypical for a pituitary adenoma (eg, constitutional symptoms), known diagnoses of hematologic malignancies, or rapid tumor growth and invasion on imaging. Gadolinium-enhanced MRI should be pursued if nonenhanced MRI is nondiagnostic. Transsphenoidal biopsy can be considered for diagnosis. Malignancy-directed systemic therapy may improve hypopituitarism and radiographic abnormalities on MRI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".