Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine le...
Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning
About this item
Full title
Author / Creator
Bai, Xuexue , Feng, Ming , Ma, Wenbin and Wang, Shiyong
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 201...
Alternative Titles
Full title
Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_e62fc44de1e44d1db8f2cf0461b478ff
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e62fc44de1e44d1db8f2cf0461b478ff
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-00758-0
