ArticleFrontiers in endocrinology2021
Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Prediction of remission in cushing's disease using artificial intelligence: A systematic review and meta-analysis.Neurosurgical review · 2026Pooled it
- Tracing the history of clinical practice of liquid biopsy: a bibliometric analysis.Frontiers in immunology · 2025Pooled it
- 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
- Radiomics for Preoperative Assessment of Pituitary Adenoma Consistency with T2-Weighted MRI: A Multicenter Study.Journal of neurological surgery. Part B, Skull base · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical 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
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review.Health data science · 2024Review
- The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review.Journal of neurological surgery. Part B, Skull base · 2023Article
- Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.Brain sciences · 2023Review
- Emerging diagnostic methods and imaging modalities in cushing's syndrome.Frontiers in endocrinology · 2023Review
- Predicting delayed remission in Cushing's disease using radiomics models: a multi-center study.Frontiers in oncology · 2023Article
- Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing's Disease: Application of Word Embedding.Frontiers in oncology · 2021Article
- The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas.Frontiers in oncology · 2021Review
- An Integrated Clinical Score to Predict Remission in Cushing's Disease.Indian journal of endocrinology and metabolismArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD). Purpose: Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods: A total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC. Results: The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion: We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.
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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.