ArticleEuropean radiology2018
Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Review
- Magnetic resonance imaging-based morphometric differences in the normal pituitary gland with functional and nonfunctional pituitary adenomas.Surgical neurology international · 2026Article
- Preoperatively Predicting PIT1 Expression in Pituitary Adenomas Using Habitat, Intra-tumoral and Peri-tumoral Radiomics Based on MRI.Journal of imaging informatics in medicine · 2025Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study.BMC cancer · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma.Scientific reports · 2025Article
- A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.Neuroradiology · 2025Article
- Rathke's cleft cyst: From history to molecular genetics.Reviews in endocrine & metabolic disorders · 2025Review
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Radiomics-based multiple machine learning approaches for investigating medial wall invasion of the cavernous sinus in pituitary adenomas.Frontiers in oncology · 2025Article
- Review
- Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.Pituitary · 2024Article
- Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?Acta neurochirurgica · 2024Article
- Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives.Journal of clinical medicine · 2024Review
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Differentiation of silent corticotroph pituitary neuroendocrine tumors (PitNETs) from non-functioning PitNETs using kinetic analysis of dynamic MRI.Japanese journal of radiology · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach.
methodsWe enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images. Support vector machine trained a predictive model that was validated using a receiver operating characteristics (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.
resultsT1 image features yielded area under the curve (AUC) values of 0.8314 and 0.8042 for the training and test sets, respectively, while CE-T1 image features provided no additional contribution to the predictive model. The nomogram incorporating sex and the T1 radiomics signature yielded good calibration in the training and test sets (concordance index (CI) = 0.854 and 0.857, respectively).
conclusionThis study focused on the preoperative prediction of NFPA subtypes between NCAs and others using a radiomics approach. The developed model yielded good performance, indicating that radiomics had good potential for the preoperative diagnosis of NFPAs. KEY POINTS: • MRI may help in the pre-operative diagnosis of NFPAs subtypes • Retrospective study showed T1-weighted MRI more useful than CE-T1 in NCAs diagnosis • Treatment decision making becomes more individualised • Radiomics approach had potential for classification of NFPAs.
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