ArticlePituitary2022
Machine learning as a clinical decision support tool for patients with acromegaly.
Article in Pituitary, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Reported baseline variables in transsphenoidal surgery for pituitary adenoma over a 30 year period: a systematic review.Pituitary · 2023Pooled it
- Endoscopic endonasal transcavernous surgery for knosp grade 3-4 growth hormone-secreting pituitary neuroendocrine tumors: surgical outcomes and predictors of biochemical remission.Neurosurgical review · 2026Article
- Acromegaly diagnosis.The Journal of clinical endocrinology and metabolism · 2026Review
- Adenoma receptors and histologic characteristics determining management outcomes.The Journal of clinical endocrinology and metabolism · 2026Review
- Consensus on acromegaly therapeutic outcomes: an update.Nature reviews. Endocrinology · 2025Review
- Artificial intelligence in acromegaly: Why, when and how.Pituitary · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.Pituitary · 2024Article
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- Current and Future Advances in Surgical Therapy for Pituitary Adenoma.Endocrine reviews · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis.
methodsWe studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used.
resultsOne-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance.
conclusionsML models may serve as valuable tools in the prediction of remission and SRL resistance.
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