Cargando…
Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer
SIMPLE SUMMARY: Machine learning (ML) is a form of artificial intelligence that could be used to enhance the efficiency of developing accurate prediction models for survival outcomes with cancer medicines, which is critical in informing disease prognosis and care planning. We used data from two rece...
Autores principales: | Abuhelwa, Ahmad Y., Kichenadasse, Ganessan, McKinnon, Ross A., Rowland, Andrew, Hopkins, Ashley M., Sorich, Michael J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122430/ https://www.ncbi.nlm.nih.gov/pubmed/33919237 http://dx.doi.org/10.3390/cancers13092001 |
Ejemplares similares
-
Value of the Lung Immune Prognostic Index in Patients with Non-Small Cell Lung Cancer Initiating First-Line Atezolizumab Combination Therapy: Subgroup Analysis of the IMPOWER150 Trial
por: Hopkins, Ashley M., et al.
Publicado: (2021) -
Reply to Auclin et al. Comment on “Hopkins et al. Value of the Lung Immune Prognostic Index in Patients with Non-Small Cell Lung Cancer Initiating First-Line Atezolizumab Combination Therapy: Subgroup Analysis of the IMPOWER150 Trial. Cancers 2021, 13, 1176”
por: Hopkins, Ashley M., et al.
Publicado: (2021) -
Predicting response and toxicity to immune checkpoint inhibitors using routinely available blood and clinical markers
por: Hopkins, Ashley M, et al.
Publicado: (2017) -
C-reactive protein provides superior prognostic accuracy than the IMDC risk model in renal cell carcinoma treated with Atezolizumab/Bevacizumab
por: Abuhelwa, Ahmad Y., et al.
Publicado: (2022) -
The obesity paradox in early and advanced HER2 positive breast cancer: pooled analysis of clinical trial data
por: Modi, Natansh D., et al.
Publicado: (2021)