ArticleJournal of personalized medicine2022
Machine Learning Prediction of Visual Outcome after Surgical Decompression of Sellar Region Tumors.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Clinical Features and Key Prognostic Indicators of Growth Hormone-Secreting Pituitary Adenomas: A Retrospective Study of 344 Cases.Current oncology (Toronto, Ont.) · 2026Article
- Eye Selection Criteria's Influence in the Value of Pituitary Macroadenoma Management Biomarkers: Preliminary Findings.Journal of clinical medicine · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- A clinical practical model for preoperative prediction of visual outcome for pituitary adenoma patients in a retrospective and prospective study.Frontiers in endocrinology · 2024Article
- Severe hypernatremia during postoperative care in patients with craniopharyngioma.Endocrine connections · 2023Article
- Predictive Modeling of Long-Term Glaucoma Progression Based on Initial Ophthalmic Data and Optic Nerve Head Characteristics.Translational vision science & technology · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionThis study aims to develop a machine learning-based model integrating clinical and ophthalmic features to predict visual outcomes after transsphenoidal resection of sellar region tumors.
methodsAdult patients with optic chiasm compression by a sellar region tumor were examined to develop a model, and an independent retrospective cohort and a prospective cohort were used to validate our model. Predictors included demographic information, and ophthalmic and laboratory test results. We defined "recovery" as more than 5% for a
resultsWe included 159 patients (57.2% male), and the mean age was 42.3 years old. Among them, 96 patients were craniopharyngiomas and 63 patients were pituitary adenomas. Larger tumors (3.3 cm vs. 2.8 cm in tumor height) and craniopharyngiomas (73.6%) were associated with a worse prognosis (
conclusionSHAP score can be a valuable resource for healthcare professionals in identifying patients with a higher risk of persistent visual deficit. The large-scale and prospective application of the proposed model would strengthen its clinical utility and universal applicability in practice.
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Registered trials
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