ArticleFrontiers in endocrinology2022
Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- Review
- AI-Powered MRI Radiomics and Deep Learning for Preoperative Prediction of Cavernous Sinus Invasion in Pituitary Adenomas: A Clinically Oriented Review of Current Evidence.Neuroimage. Reports · 2026Review
- Prolactinoma-associated headache: Clinical and radiological correlates and long-term outcomes.Pituitary · 2026Article
- Predictors of favorable long-term outcomes in first-line surgery for microprolactinomas.Journal of neuro-oncology · 2025Article
- Gender differences in prolactin thresholds and their association with lactotroph adenoma invasiveness for potential treatment considerations.Scientific reports · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Characteristics and outcomes of men with erectile dysfunction as the presenting symptom due to a lactotroph adenoma.Acta neurochirurgica · 2024Article
- The diagnostic yield of inferior petrosal sinus sampling in Cushing syndrome in the era of ovine CRH shortage.Acta neurochirurgica · 2024Article
- Gender-specific prolactin thresholds to determine prolactinoma size: a novel Bayesian approach and its clinical utility.Frontiers in surgery · 2024Article
- Current and Future Advances in Surgical Therapy for Pituitary Adenoma.Endocrine reviews · 2023Article
- Role of Immediate Postoperative Prolactin Measurement in Female Prolactinoma Patients: Predicting Long-Term Remission After Complete Tumor Removal.Brain tumor research and treatment · 2023Article
- Prognostic Models in Growth-Hormone- and Prolactin-Secreting Pituitary Neuroendocrine Tumors: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Prolactinomas in adolescent and elderly patients-A comparative long-term analysis.Frontiers in surgery · 2023Article
- Commentary: Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFirst-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making.
objectiveTo evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia.
methodsBy jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked
resultsWe demonstrate that for upfront surgery in prolactinoma patients there are not a
conclusionsThis study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers.
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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.