ArticleFrontiers in endocrinology2024
A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Bilateral visual failure with optic tract involvement in sellar-suprasellar lymphoma: illustrative case.Journal of neurosurgery. Case lessons · 2026Article
- Prognostic factors in the surgical treatment of non-functioning pituitary adenomas: a retrospective cohort study.Archives of endocrinology and metabolism · 2026Article
- Relationship between retinal nerve fibre thickness, suprasellar extension and post-operative visual outcome in pituitary macroadenoma.International journal of ophthalmology · 2026Article
- Predictors of delayed hyponatremia after endoscopic transsphenoidal surgery for non-functioning pituitary adenomas.Frontiers in endocrinology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Preoperative prediction of visual recovery after pituitary adenoma resection surgery remains challenging. This study aimed to investigate the value of clinical and radiological features in preoperatively predicting visual outcomes after surgery. Methods: Patients undergoing endoscopic transsphenoidal surgery (ETS) for pituitary adenoma were included in this retrospective and prospective study. Preoperative MRI, visual acuity, visual field, and postoperative visual recovery data were collected. Logistic regression analysis was used to assess the importance of clinical and MRI features, and a prediction model was developed. Results: The cohort included 198 patients (150 retrospective, 48 prospective). In the retrospective data, visual recovery was observed in 111 patients (74.0%), while non-recovery was observed in 39 patients (26.0%). In the prospective data, visual recovery was observed in 27 patients (56.25%) and non-recovery in 21 patients (43.75%). Blindness, headache, adenoma area, and adenoma upward growth distance were negatively correlated with visual recovery (p < 0.05), while the pituitary gland area was positively correlated (p = 0.001). Logistic regression selected three clinical features: blindness, headache, and visual impairment course. Two additional imaging features, pituitary gland maximum area, and adenoma maximum area, were incorporated into the prediction model. The area under the curve of the prediction model was 0.944 in the retrospective cohort and 0.857 in the prospective cohort. Accuracy was 88% and 81.25%, respectively. Conclusion: This study successfully developed a clinical practical model combining clinical and radiological features to preoperatively predict visual recovery for patients with pituitary adenoma. The model has the potential to provide personalized counseling for individual patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.