Bibliographic Details
| Title: |
Predicting Pituitary Adenoma Consistency Using Radiomic Data Mining and Machine Learning on T2-Weighted MRI: A Multicenter Retrospective Study. |
| Authors: |
Agosti, Edoardo, Ugga, Lorenzo, Mangili, Marcello, Cuocolo, Renato, Rampinelli, Vittorio, Veiceschi, Pierlorenzo, Cappelletti, Marina, Panciani, Pier Paolo, Cavallo, Luigi Maria, Locatelli, Davide, Fiorindi, Alessandro, Fontanella, Marco Maria |
| Source: |
Journal of Neurological Surgery. Part B. Skull Base; 2025 Supplement 1, Vol. 86, pS1-S576, 576p |
| Subject Terms: |
MACHINE learning, TEXTURE analysis (Image processing), RECEIVER operating characteristic curves, FEATURE extraction, FEATURE selection |
| Abstract: |
The article discusses a study that utilized radiomic data mining and machine learning on T2-weighted MRI to predict the consistency of pituitary adenomas (PAs) in patients undergoing endonasal endoscopic surgery. The research, conducted at multiple centers, involved the classification of PAs as soft, fibrous, or mixed based on intraoperative findings. By analyzing texture features extracted from MRI images, a machine learning model achieved high accuracy in distinguishing between soft and fibrous PAs, showing promise for pre-surgical planning. The study suggests that this tool could be valuable with further development and validation on larger datasets. [Extracted from the article] |
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| Database: |
Complementary Index |
