Cargando…
Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors
BACKGROUND: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a...
Autores principales: | Chang, Enoch, Joel, Marina, Chang, Hannah Y., Du, Justin, Khanna, Omaditya, Omuro, Antonio, Chiang, Veronica, Aneja, Sanjay |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654896/ https://www.ncbi.nlm.nih.gov/pubmed/33173902 http://dx.doi.org/10.1101/2020.11.04.20226159 |
Ejemplares similares
-
Comparison of radiomic feature aggregation methods for patients with multiple tumors
por: Chang, Enoch, et al.
Publicado: (2021) -
Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
por: Joel, Marina Z., et al.
Publicado: (2022) -
The importance of feature aggregation in radiomics: a head and neck cancer study
por: Fontaine, Pierre, et al.
Publicado: (2020) -
Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
por: Joel, Marina Z., et al.
Publicado: (2023) -
PACS-Integrated Tools for Peritumoral Edema Volumetrics Provide Additional Information to RANO-BM-Based Assessment of Lung Cancer Brain Metastases after Stereotactic Radiotherapy: A Pilot Study
por: Kaur, Manpreet, et al.
Publicado: (2023)