Fractionated Stereotactic Radiotherapy in the Treatment of Pituitary Macroadenomas
Bibliographic record
Abstract
BACKGROUND: The use of fractionated stereotactic radiotherapy (FSRT) has evolved with technical advances in noninvasive immobilization, radiation delivery, and image guidance. The application of FSRT to pituitary tumours is aimed at reducing toxicity through improved dose conformality and reduced treatment margins. The aim of the present paper is to report our own experience and to review the published data on FSRT for pituitary macroadenomas. METHODS: Between September 2000 and October 2005, 13 patients with pituitary macroadenoma underwent FSRT at our institution. In 12 patients, radiotherapy treatment followed surgical resection (transsphenoidal resection in 8, frontal craniotomy in 3, and multiple transsphenoidal resections followed by craniotomy in 1). In 4 patients, the tumours were functional (2 adrenocorticotropic hormone-secreting, 1 prolactinoma, and 1 growth hormone-secreting); the tumours in the remaining patients were clinically non-secretory. Before radiation, 3 patients had panhypopituitarism, and 6 patients had visual field defects. All patients were treated with FSRT using non-coplanar micro-multileaf collimation portals. A median dose of 50.4 Gy (range: 45-60 Gy) was prescribed to the 76.9%-95.2% isodose surface and delivered in 1.8-Gy fractions. The median planning target volume (gross tumour plus 3 mm) was 33.5 cm3 (range: 3.2-75 cm3). RESULTS: After a median follow-up of 24 months (range: 6-60 months), local control was 100%. One patient achieved clinical complete response. Treatment was well tolerated acutely for all patients. Neither radiation-induced optic neuropathy nor any radiation-related endocrine dysfunction was observed in our patients. CONCLUSIONS: In accordance with published series, we found FSRT to be safe and effective in the management of large pituitary macroadenomas.
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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) | 0.000 | 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) | 0.001 | 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".