Cargando…
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation
OBJECTIVE: This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model...
Autores principales: | Zhu, Jian-man, Sun, Lei, Wang, Linjing, Zhou, Tong-Chong, Yuan, Yawei, Zhen, Xin, Liao, Zhi-Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008953/ https://www.ncbi.nlm.nih.gov/pubmed/35422007 http://dx.doi.org/10.1186/s13104-022-06019-x |
Ejemplares similares
-
MRI radiomics predicts progression-free survival in prostate cancer
por: Jia, Yushan, et al.
Publicado: (2022) -
Estimating quality adjusted progression free survival of first-line treatments for EGFR mutation positive non small cell lung cancer patients in The Netherlands
por: Verduyn, S Cora, et al.
Publicado: (2012) -
Corrigendum: MRI radiomics predicts progression-free survival in prostate cancer
por: Jia, Yushan, et al.
Publicado: (2023) -
Thick-wall cavity predicts worse progression-free survival in lung adenocarcinoma treated with first-line EGFR-TKIs
por: Zhou, Fei, et al.
Publicado: (2018) -
Survival benefits from afatinib compared with gefitinib and erlotinib among patients with common
EGFR
mutation in first‐line setting
por: Kwok, Wang Chun, et al.
Publicado: (2022)