Development of a clinical-radiological nomogram for predicting severe postoperative peritumoral brain edema following intracranial meningioma resection
Utilizing conventional logistic regression within machine learning frameworks, we developed a robust prediction model. The clinical-radiological nomogram, based on conventional logistic regression, integrated clinical characteristics to enhance the prediction accuracy for severe PTBE in patients following intracranial meningioma resection. This nomogram showed promise in aiding clinicians to create personalized and optimal treatment plans by providing precise forecasts of severe PTBE.
Authors
- Bo Chen (ORCID: https://orcid.org/0000-0003-2702-3758)
- Ting Wu (ORCID: https://orcid.org/0009-0002-4931-2251)
- Eryi Sun (ORCID: https://orcid.org/0000-0002-6772-1246)
- Dianjun Wang (ORCID: https://orcid.org/0000-0002-5620-3320)
- Shan Xiuhong
- Nan Zhao (ORCID: https://orcid.org/0000-0002-1001-716X)
- Siyuan Lu (ORCID: https://orcid.org/0009-0004-8169-8259)
- Yan Deng (ORCID: https://orcid.org/0000-0002-9056-2554)
- Geng Ao
Institutions
- Jiangsu University (CN)
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Affiliated Hospital of Jiangsu University (CN)
- Northern Jiangsu People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-01-16
- DOI
- https://doi.org/10.3389/fneur.2024.1478213
- Citations
- 3
- Primary Topic
- Meningioma and schwannoma management
- Type
- article
- Field-Weighted Citation Impact
- 4.06