首页|Kocaeli University Reports Findings in Adenomas (A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts)
Kocaeli University Reports Findings in Adenomas (A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts)
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New research on Adenomas is the subject of a report. According to news reporting originating from Kocaeli, Turkey, by NewsRx correspondents, research stated, "To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). The study involved 65 patients diagnosed with either CPA or RCCs." Our news editors obtained a quote from the research from Kocaeli University, "Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2- weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only ( = 0.019)."
KocaeliTurkeyEurasiaAdenomasCyborgsEmerging TechnologiesHealth and MedicineMachine LearningOncologySupport Vector Machines
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