ArticleBrain & spine2025
Preoperative assessment of tumor consistency and gross total resection in pituitary adenoma: Radiomic analysis of T2-weighted MRI and interpretation of contributing radiomic features.
Article in Brain & spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based models for preoperative prediction of pituitary adenoma consistency: a systematic review and meta-analysis.Acta neurochirurgica · 2026Pooled it
- 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
- Article
- Clinically informed preoperative risk stratification for MRI-defined non gross-total resection in nonfunctioning pituitary neuroendocrine tumors: a single-center internal validation study.Frontiers in physiology · 2026Article
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8 authors.
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Abstract
Background: Preoperative knowledge of tumor consistency and the likelihood of gross total resection (GTR) would greatly benefit planning of pituitary adenoma surgery, however, no reliable methods currently exist. Objectives: To evaluate the utility of radiomic analysis of MRI for predicting tumor consistency and GTR. To explore the interpretability of contributing radiomic features. Methods: Patients undergoing first endoscopic surgery for pituitary macroadenomas were included. Tumor consistency was assessed intraoperatively, GTR was assessed based on postoperative MRI. Radiomic features were extracted from axial T2-weighted MRI. Low-variability and highly intercorrelated features were removed. Random Forest Classifiers were optimized using 70 % of patient data and evaluated on the remaining 30 %. Relative feature importance was assessed using the Gini-Simpson index. Results: 542 patients were included. GTR was achieved in 325 (60.0 %) cases, firm tumors were encountered in 122 (22.5 %) cases. There was a significant correlation between GTR and tumor consistency (67.1 % vs. 35.2 %, p < 0.001). 1688 radiomic variables were extracted, 442 were removed due to low variance and 699 due to high intercorrelation. The consistency prediction model achieved an accuracy of 81.6 % and utilized 32 features, GTR prediction model achieved 79.1 % accuracy using 73 features. Conclusions: Radiomic analysis demonstrated significant potential for preoperative evaluation of pituitary adenomas. Texture and intensity-based features were the primary contributors to consistency prediction. However, the explanation of these features was insufficient. GTR prediction was predominantly driven by shape-related features. Our findings highlight the challenges of linking radiomic features to underlying tissue properties and emphasize the need for cautious interpretation.
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