Local Control and Toxicity Assessment of Pituitary Adenoma Patients Treated with Different Radiotherapy Techniques
Bibliographic record
Abstract
Background: Pituitary adenomas often require radiotherapy (RT) for residual or recurrent disease, but optimal techniques balancing tumour control and toxicity remain debated. This prospective study compares outcomes between conventional and stereotactic RT approaches. Methods: From 2014 to 2022, 22 patients with pituitary adenomas treated with RT were retrospectively enrolled (10 conventional RT [3DCRT/IMRT/VMAT], 12 stereotactic [SRS/fSRT]). All the patients disease, treatment and follow-up details were analyzed from the medical records. Primary endpoint was 3-year local control, secondary endpoints included toxicity (CTCAE v5.0) and endocrine function assessment. Results: Stereotactic RT demonstrated superior 3-year local control (91.7% vs 80%, p=0.03) with lower hypopituitarism rates (33.3% vs 60%, p=0.02). All recurrences occurred in Knosp grade 3-4 tumours. Conventional RT was associated with higher pituitary doses (>45 Gy, OR 3.2, p=0.03 for hypopituitarism). No grade ≥3 toxicities occurred in either group. Visual complications were rare (8.3% stereotactic vs 20% conventional, p=0.39). Conclusion: Stereotactic radiotherapy provides significantly better tumour control and endocrine preservation compared to conventional techniques for pituitary adenomas, particularly for non-invasive tumours. Dose constraints are critical for minimizing toxicity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".