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Record W4412525733 · doi:10.30683/1927-7229.2025.14.04

Local Control and Toxicity Assessment of Pituitary Adenoma Patients Treated with Different Radiotherapy Techniques

2025· article· en· W4412525733 on OpenAlexvenueno aff
Arora Kanakdeepsingh Rajendrasingh, Geeta S. Narayanan, Kiran Kumar BR

Bibliographic record

VenueJournal of Analytical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsToxicityRadiation therapyPituitary adenomaMedicineInternal medicineAdenomaOncology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.314
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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