Document Type
Article
Publication Title
Cancers (Basel)
Abstract
Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (
DOI
10.3390/cancers15184636
Publication Date
9-19-2023
Recommended Citation
Farzana, Walia; Basree, Mustafa M; Diawara, Norou; Shboul, Zeina A; Dubey, Sagel; Lockhart, Marie M; Hamza, Mohamed; Palmer, Joshua D; and Iftekharuddin, Khan M, "Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients." (2023). Oncology Articles. 10.
https://scholarlyworks.ohiohealth.com/oncology-articles/10