Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI

• Published in: Biomedicines Vol. 11; no. 12; p. 3268
• Main Authors: Park, Jae Hyun, Quang, Le Thanh, Yoon, Woong, Baek, Byung Hyun, Park, Ilwoo, Kim, Seul Kee
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2023
• Subjects: Algorithms; Automation; Brain; Brain tumors; Contrast agents; Edema; Feature selection; grading; Hospitals; Machine learning; Magnetic resonance imaging; Medical research; Medicine, Experimental; Meningioma; Neuroimaging; Patients; Prediction models; radiomic features; Radiomics; Registration; Scanners; Software; South Korea; Tumors; South Korea; meningioma; radiomic features; magnetic resonance imaging; grading; machine learning

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively review...