ReviewJournal of clinical medicine2024
Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- Radiomics and artificial intelligence in pituitary adenomas (PitNETs): from imaging biomarkers to clinical translation.Reviews in endocrine & metabolic disorders · 2026Review
- Association between preoperative MRI radiomic features and methylation-defined phenotypes in non-functioning pituitary adenomas.Pituitary · 2026Article
- Radiomics for Preoperative Assessment of Pituitary Adenoma Consistency with T2-Weighted MRI: A Multicenter Study.Journal of neurological surgery. Part B, Skull base · 2026Article
- Article
- CT Radiomic Features of the Crystalline Lens and Association with Age, Hypertension and Cerebral White Matter Lesions.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Prediction of Pituitary Macroadenoma Consistency: Utilizing Demographic Data and Brain MRI Parameters.Journal of imaging informatics in medicine · 2025Article
- Challenges and opportunities for new intraoperative optical techniques in the surgical treatment of pituitary adenomas: a review.Journal of biomedical optics · 2025Review
- Mapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics.Scientific data · 2025Article
- MR radiomics in assessment of consistency of pituitary macroadenoma: can T1-weighted contrast enhanced image improve diagnostic performance of T2-weighted image?Frontiers in oncology · 2025Article
- Preoperative prediction of p53 overexpression in pituitary neuroendocrine tumors using MRI radiomics.Frontiers in neurology · 2025Article
- Prolactinoma: Navigating the Dual Challenge of Side Effects and Treatment Strategies - A Comprehensive Review.Annals of medicine and surgery (2012) · 2024Review
- An interpretable machine learning model for identifying granulation patterns in somatotroph tumors: A multi-center radiomics study.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.
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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.