Cargando…
Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment
Therapeutic outcomes in patients with metastatic colorectal cancer (mCRC) receiving bevacizumab treatment are highly variable, and a reliable predictive factor is not available. Progression‐free survival (PFS) and overall survival (OS) were recorded from an observational, prospective study after 5 y...
Autores principales: | Karatza, Eleni, Papachristos, Apostolos, Sivolapenko, Gregory B., Gonzalez, Daniel |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574729/ https://www.ncbi.nlm.nih.gov/pubmed/35851999 http://dx.doi.org/10.1002/psp4.12848 |
Ejemplares similares
-
Pharmacogenetics in Model-Based Optimization of Bevacizumab Therapy for Metastatic Colorectal Cancer
por: Papachristos, Apostolos, et al.
Publicado: (2020) -
Pharmacogenomics, Pharmacokinetics and Circulating Proteins As Biomarkers for Bevacizumab Treatment Optimization in Patients with Cancer: A Review
por: Papachristos, Apostolos, et al.
Publicado: (2020) -
VEGF-A and ICAM-1 Gene Polymorphisms as Predictors of Clinical Outcome to First-Line Bevacizumab-Based Treatment in Metastatic Colorectal Cancer
por: Papachristos, Apostolos, et al.
Publicado: (2019) -
Real-World Experience in Toxicity with Bevacizumab in Indian Cancer Patients
por: Patil, Pratik P., et al.
Publicado: (2021) -
A pharmacokinetic binding model for bevacizumab and VEGF(165) in colorectal cancer patients
por: Panoilia, Eirini, et al.
Publicado: (2015)