Cargando…
Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [(18)F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions
SIMPLE SUMMARY: Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-re...
Autores principales: | Gómez, Ober Van, Herraiz, Joaquin L., Udías, José Manuel, Haug, Alexander, Papp, Laszlo, Cioni, Dania, Neri, Emanuele |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221062/ https://www.ncbi.nlm.nih.gov/pubmed/35740588 http://dx.doi.org/10.3390/cancers14122922 |
Ejemplares similares
-
Assessing radiomic feature robustness to interpolation in (18)F-FDG PET imaging
por: Whybra, Philip, et al.
Publicado: (2019) -
Radiomic Features of (18)F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes
por: Zhou, Yeye, et al.
Publicado: (2021) -
Breast Tumor Characterization Using [(18)F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics
por: Krajnc, Denis, et al.
Publicado: (2021) -
Ability of (18)F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma
por: Ou, Xuejin, et al.
Publicado: (2019) -
Predicting EGFR mutation subtypes in lung adenocarcinoma using (18)F-FDG PET/CT radiomic features
por: Liu, Qiufang, et al.
Publicado: (2020)