Cargando…
A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer
SIMPLE SUMMARY: In this study, we explored the potential of using F-18 fluorodeoxyglucose positron emission tomography (FDG PET), to predict the genetic characteristics and prognosis of patients with stage IV colorectal cancer. We used a machine learning approach to analyze association between image...
Autores principales: | Lee, Hyunjong, Moon, Seung Hwan, Hong, Jung Yong, Lee, Jeeyun, Hyun, Seung Hyup |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416826/ https://www.ncbi.nlm.nih.gov/pubmed/37568657 http://dx.doi.org/10.3390/cancers15153841 |
Ejemplares similares
-
Cluster analysis of autoencoder-extracted FDG PET/CT features identifies multiple myeloma patients with poor prognosis
por: Lee, Hyunjong, et al.
Publicado: (2023) -
Metabolic bulk volume from FDG PET as an independent predictor of progression-free survival in follicular lymphoma
por: So, Heejune, et al.
Publicado: (2023) -
Value of pre-treatment (18)F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer
por: Wang, Na, et al.
Publicado: (2023) -
Metabolism-Associated Gene Signatures for FDG Avidity on PET/CT and Prognostic Validation in Hepatocellular Carcinoma
por: Lee, Hyunjong, et al.
Publicado: (2022) -
Semi-quantitative FDG parameters predict survival in multiple myeloma patients without autologous stem cell transplantation
por: Lee, Hyunjong, et al.
Publicado: (2023)