Immune Checkpoint Inhibitor-Induced Hypophysitis: Lessons Learnt from a Large Cancer Cohort
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) can cause pituitary dysfunction due to hypophysitis. We aimed to characterize ICI-induced hypophysitis and examine its association with overall survival in this single-center retrospective cohort study of adult patients with cancer who received an ICI from January 1, 2012 through December 31, 2016. A total of 896 patients were identified who received ipilimumab alone (n=120); ipilimumab and nivolumab (n=50); ipilimumab before or after pembrolizumab (n=70); pembrolizumab alone (n=406); and nivolumab alone (n=250). Twenty-six patients (2.9%) developed hypophysitis after a median of 2.3 months. Median age at the start of ICI was 57.9 years and 54% were men. Hypophysitis occurred in 7.9% of patients receiving ipilimumab alone or in combination or sequence with a programmed cell death protein 1 inhibitor; 1.7% after pembrolizumab alone, never after nivolumab alone. Secondary adrenal insufficiency occurred in all hypophysitis cases. Use of ipilimumab alone or in combination was associated with pituitary enlargement on imaging and mass effects more frequently than pembrolizumab alone. Occurrence of hypophysitis was associated with improved overall survival by univariate analysis (median 50.7 vs 16.5 months; p=0.015) but this association was not observed in multivariable landmark survival analysis (HR for mortality 0.75; 95% CI 0.38 to 1.30; p=0.34) after adjusting for age, sex and malignancy type. To conclude, hypophysitis occurred most frequently after ipilimumab and manifested as anterior hypopituitarism affecting the corticotrophs more commonly than thyrotrophs and gonadotrophs. Mass effects and pituitary enlargement occurred more frequently in ipilimumab-induced hypophysitis. The association of hypophysitis with overall survival needs further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".