ArticleNeuroradiology2020
Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.
Article in Neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 6 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis.Acta neurochirurgica · 2026Pooled it
- Radiomics and artificial intelligence for predicting pituitary neuroendocrine tumor consistency: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.Neuroradiology · 2021Pooled it
- Is endoscopic diving technique effective in surgical management of functioning pituitary adenomas? A prospective randomized study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Trial
- Radiomics-based prediction of pituitary adenoma consistency: a systematic review and meta-analysis.La Radiologia medica · 2026Article
- Radiomics and artificial intelligence in pituitary adenomas (PitNETs): from imaging biomarkers to clinical translation.Reviews in endocrine & metabolic disorders · 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
- MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.European radiology experimental · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- 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
- Preoperative prediction of pituitary neuroendocrine tumor consistency based on multiparametric MRI radiomics: a multicenter study.BMC cancer · 2025Article
- MRI-based interpretable clinicoradiological and radiomics machine learning model for preoperative prediction of pituitary macroadenomas consistency: a dual-center study.Neuroradiology · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.Journal of imaging informatics in medicine · 2025Article
- Radiomics-based multiple machine learning approaches for investigating medial wall invasion of the cavernous sinus in pituitary adenomas.Frontiers in oncology · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery.
methodsData of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings.
resultsA total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance (n = 4) and highly intercorrelated (n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99.
conclusionPreoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency.
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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.