ArticleEndocrine2024
Automated volumetric assessment of pituitary adenoma.
Article in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Association between preoperative MRI radiomic features and methylation-defined phenotypes in non-functioning pituitary adenomas.Pituitary · 2026Article
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- Preoperative Prediction of Non-functional Pituitary Neuroendocrine Tumors and Posterior Pituitary Tumors Based on MRI Radiomic Features.Journal of imaging informatics in medicine · 2026Article
- Automated assessment of Knosp grade from pituitary adenoma MRI: Experimental comparison of a rule-based and a machine learning-based approach.Brain & spine · 2026Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.Journal of clinical medicine · 2025Review
- Utilities of 3D technology for planning endoscopic endonasal approaches. Case series of anatomical-imaging review.Journal of surgical case reports · 2025Article
- Fully automated grading of pituitary adenoma.Neuroimage. Reports · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAssessment of pituitary adenoma (PA) volume and extent of resection (EOR) through manual segmentation is time-consuming and likely suffers from poor interrater agreement, especially postoperatively. Automated tumor segmentation and volumetry by use of deep learning techniques may provide more objective and quick volumetry.
methodsWe developed an automated volumetry pipeline for pituitary adenoma. Preoperative and three-month postoperative T1-weighted, contrast-enhanced magnetic resonance imaging (MRI) with manual segmentations were used for model training. After adequate preprocessing, an ensemble of convolutional neural networks (CNNs) was trained and validated for preoperative and postoperative automated segmentation of tumor tissue. Generalization was evaluated on a separate holdout set.
resultsIn total, 193 image sets were used for training and 20 were held out for validation. At validation using the holdout set, our models (preoperative / postoperative) demonstrated a median Dice score of 0.71 (0.27) / 0 (0), a mean Jaccard score of 0.53 ± 0.21/0.030 ± 0.085 and a mean 95
conclusionsOur volumetry pipeline demonstrated its ability to accurately segment pituitary adenomas. This is highly valuable for lesion detection and evaluation of progression of pituitary incidentalomas. Postoperatively, however, objective and precise detection of residual tumor remains less successful. Larger datasets, more diverse data, and more elaborate modeling could potentially improve performance.
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