Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

PURPOSE: To construct machine learning models for predicting progression free survival (PFS) and overall survival (OS) with esophageal squamous cell carcinoma (ESCC) patients. METHODS: 204 ESCC patients were randomly divided into training cohort (n = 143) and test cohort (n = 61) according to the ra...

Descripción completa

Detalles Bibliográficos
Autores principales: Cui, Yongbin, Li, Zhengjiang, Xiang, Mingyue, Han, Dali, Yin, Yong, Ma, Changsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795769/
https://www.ncbi.nlm.nih.gov/pubmed/36575480
http://dx.doi.org/10.1186/s13014-022-02186-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795769/
https://www.ncbi.nlm.nih.gov/pubmed/36575480
http://dx.doi.org/10.1186/s13014-022-02186-0

Ejemplares similares