ArticleEndocrine connections2019
A systematic review on machine learning in sellar region diseases: quality and reporting items.
Article in Endocrine connections, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 8 of them syntheses that pooled it.
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
25 citing papers in PubMed, 8 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.BJS open · 2023Pooled it
- Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.BMC medical research methodology · 2022Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature.Frontiers in immunology · 2022Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Predicting breast cancer 5-year survival using machine learning: A systematic review.PloS one · 2021Pooled it
- Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.Heart failure reviews · 2021Pooled it
- Critical Appraisal Tools for Evaluating Artificial Intelligence in Clinical Studies: Scoping Review.Journal of medical Internet research · 2025Article
- Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.BMC medical imaging · 2025Article
- Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.Scientific reports · 2025Article
- Severe hypernatremia during postoperative care in patients with craniopharyngioma.Endocrine connections · 2023Article
- Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.Journal of clinical medicine · 2023Review
- Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective.Journal of medical Internet research · 2023Article
- Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.Brain sciences · 2023Review
- Predicting delayed remission in Cushing's disease using radiomics models: a multi-center study.Frontiers in oncology · 2023Article
- The "chameleon" sellar lesions: a case report of unexpected sellar lesions.Frontiers in neurology · 2023Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review.JMIR medical informatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMachine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies.
methodsPubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance.
resultsSeventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results.
conclusionsMachine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.
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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.