ReviewFrontiers in endocrinology2024
The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.
Review in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.BMC endocrine disorders · 2025Pooled it
- Comparison of gadobutrol and meglumine gadoterate for dynamic contrast-enhanced MRI of pituitary macroadenomas.Scientific reports · 2025Trial
- MRI-based radiomics prediction of somatostatin receptor ligand response in acromegaly: an automated pipeline study.Journal of the Endocrine Society · 2026Article
- Radiomics and artificial intelligence in pituitary adenomas (PitNETs): from imaging biomarkers to clinical translation.Reviews in endocrine & metabolic disorders · 2026Review
- Human biomarker navigator.iMeta · 2026Review
- A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.Neuroradiology · 2026Article
- The Transcription Factor SOX6 Regulates Autophagy Levels Through LAMP5 to Inhibit the Development of Pituitary Adenomas.Applied biochemistry and biotechnology · 2026Article
- 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
- [Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?]Innere Medizin (Heidelberg, Germany) · 2026Review
- Review
- AI-powered radiomics for detecting rare pituitary stalk interruption with growth hormone-insulin-like factor desynchrony.Annals of medicine and surgery (2012) · 2026Article
- New Sparks and Spots: Molecular Imaging with Positron Emission Tomography Will Change Management of Cushing's Disease.Endocrinology and metabolism (Seoul, Korea) · 2026Review
- Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging.Frontiers in radiology · 2026Review
- Machine Learning Prediction of Pituitary Macroadenoma Consistency: Utilizing Demographic Data and Brain MRI Parameters.Journal of imaging informatics in medicine · 2025Article
- Radiological Differentiation of Pituitary Adenomas From Other Sellar Masses: A Systematic Review.Cureus · 2025Review
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Challenges and opportunities for new intraoperative optical techniques in the surgical treatment of pituitary adenomas: a review.Journal of biomedical optics · 2025Review
- Preoperative prediction of p53 overexpression in pituitary neuroendocrine tumors using MRI radiomics.Frontiers in neurology · 2025Article
- Optical coherence tomography and electroretinography in pituitary macroadenomas: a 12-month analysis by age and tumor type.Frontiers in endocrinology · 2025Observational
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.
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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.