Cargando…
Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies
Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we...
Autores principales: | Yoon, Jin H., Sun, Shawn H., Xiao, Manjun, Yang, Hao, Lu, Lin, Li, Yajun, Schwartz, Lawrence H., Zhao, Binsheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707549/ https://www.ncbi.nlm.nih.gov/pubmed/34941646 http://dx.doi.org/10.3390/tomography7040074 |
Ejemplares similares
-
CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study
por: Li, Yajun, et al.
Publicado: (2018) -
Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings
por: Lu, Lin, et al.
Publicado: (2016) -
Uncontrolled Confounders May Lead to False or Overvalued Radiomics Signature: A Proof of Concept Using Survival Analysis in a Multicenter Cohort of Kidney Cancer
por: Lu, Lin, et al.
Publicado: (2021) -
Reliability of Radiomic Features Across Multiple Abdominal CT Image Acquisition Settings: A Pilot Study Using ACR CT Phantom
por: Lu, Lin, et al.
Publicado: (2019) -
Toward radiomics for assessment of response to systemic therapies in lung cancer
por: Sun, Shawn, et al.
Publicado: (2020)