Your browser doesn't support javascript.
loading
Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.
Farzana, Walia; Basree, Mustafa M; Diawara, Norou; Shboul, Zeina A; Dubey, Sagel; Lockhart, Marie M; Hamza, Mohamed; Palmer, Joshua D; Iftekharuddin, Khan M.
Afiliación
  • Farzana W; Vision Lab, Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
  • Basree MM; Department of Internal Medicine, OhioHealth Riverside Methodist Hospital, Columbus, OH 43214, USA.
  • Diawara N; Department of Mathematics & Statistics, Old Dominion University, Norfolk, VA 23529, USA.
  • Shboul ZA; Vision Lab, Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
  • Dubey S; Department of Internal Medicine, OhioHealth Riverside Methodist Hospital, Columbus, OH 43214, USA.
  • Lockhart MM; OhioHealth Research Institute, Columbus, OH 43214, USA.
  • Hamza M; Department of Neurology, OhioHealth, Columbus, OH 43214, USA.
  • Palmer JD; Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA.
  • Iftekharuddin KM; Vision Lab, Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Cancers (Basel) ; 15(18)2023 Sep 19.
Article en En | MEDLINE | ID: mdl-37760604
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 (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancers (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancers (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos