Cargando…
Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software
OBJECTIVE: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MATERIALS AND METHODS: MR images of 45 GBM patients (29 males, 16 females) were...
Autores principales: | Lee, Myungeun, Woo, Boyeong, Kuo, Michael D., Jamshidi, Neema, Kim, Jong Hyo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390619/ https://www.ncbi.nlm.nih.gov/pubmed/28458602 http://dx.doi.org/10.3348/kjr.2017.18.3.498 |
Ejemplares similares
-
MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
por: Long, Hao, et al.
Publicado: (2023) -
Modern Industrial Automation Software Design
por: Wang, Lingfeng, et al.
Publicado: (2006) -
Post-operative glioblastoma multiforme segmentation with uncertainty estimation
por: Holtzman Gazit, Michal, et al.
Publicado: (2022) -
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
por: Lao, Jiangwei, et al.
Publicado: (2017) -
A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams
por: Senthilvelan, Jayasuriya, et al.
Publicado: (2022)