Bibliographic Details
| Title: |
Machine Learning with Radiomics To Predict Who Pathologic Grade of Meningiomas from Preoperative MRI: A Multicenter Study. |
| Authors: |
Adil, Syed M.1 (AUTHOR), Warman, Pranav I.1 (AUTHOR), Rangwala, Kaizar1 (AUTHOR), Seas, Andreas1 (AUTHOR), Zachem, Tanner J.1 (AUTHOR), Abdelgadir, Jihad1 (AUTHOR), Calabrese, Evan1 (AUTHOR), Codd, Patrick J.1 (AUTHOR), Patel, Anoop1 (AUTHOR), Zomorodi, Ali1 (AUTHOR) |
| Source: |
Journal of Neurological Surgery. Part B. Skull Base. 2025 Supplement 1, Vol. 86, pS1-S576. 576p. |
| Subject Terms: |
*MACHINE learning, *RECEIVER operating characteristic curves, *RADIOMICS, *BRAIN tumors, *TUMOR grading |
| Abstract: |
The article "Machine Learning with Radiomics To Predict WHO Pathologic Grade of Meningiomas from Preoperative MRI: A Multicenter Study" published in the Journal of Neurological Surgery. Part B. Skull Base discusses the use of machine learning and radiomics features derived from preoperative MRIs to predict the World Health Organization (WHO) pathologic grade of meningiomas. The study involved 700 patients from six hospitals, and the machine learning model achieved an AUROC of 0.70, sensitivity of 68.5%, and specificity of 69.3%. The findings suggest that this approach could help guide surgical strategies, surveillance decisions, and patient counseling in the future. [Extracted from the article] |
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| Database: |
Academic Search Complete |
