Treatment of an Aggressive Gonadotroph Pituitary Neuroendocrine Tumor With 177Lutetium DOTATATE Radionuclide Therapy
Bibliographic record
Abstract
Abstract Aggressive pituitary neuroendocrine tumors (PitNETs) present significant morbidity, and multimodal therapies including surgery, radiotherapy, and medications are frequently required. Chemotherapy, particularly temozolomide, is often pursued for tumors that progress despite these treatments. Although peptide receptor radionuclide therapy (PRRT) using radiolabeled somatostatin analogs is approved for the treatment of well-differentiated gastrointestinal neuroendocrine tumors, its use in aggressive PitNETs is limited. We describe the case of a 65-year-old man who presented with vision changes and hypopituitarism at age 33 secondary to a nonfunctioning gonadotroph PitNET. His initial treatment included a craniotomy followed by radiation therapy. With tumor regrowth, he required transsphenoidal surgeries at age 44 and age 52. At age 56, further tumor regrowth and a positive octreotide scan prompted treatment with long-acting octreotide for 1 year. Given absent tumor response, 12 cycles (4 treatment cycles and 8 maintenance cycles) of PRRT with 177Lutetium-DOTATATE were pursued. This resulted in partial response with significant tumor shrinkage. Notably, there was no tumor regrowth 40 months after treatment discontinuation. This is only the second report on the effectiveness of PRRT in patients with aggressive gonadotroph PitNETs. We also provide an overview of PRRT for PitNETs and describe clinical outcomes previously reported in the literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".