Cargando…
Assessing robustness of radiomic features by image perturbation
Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accura...
Autores principales: | Zwanenburg, Alex, Leger, Stefan, Agolli, Linda, Pilz, Karoline, Troost, Esther G. C., Richter, Christian, Löck, Steffen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345842/ https://www.ncbi.nlm.nih.gov/pubmed/30679599 http://dx.doi.org/10.1038/s41598-018-36938-4 |
Ejemplares similares
-
Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction
por: Starke, Sebastian, et al.
Publicado: (2023) -
A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling
por: Leger, Stefan, et al.
Publicado: (2017) -
Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models
por: Shahzadi, Iram, et al.
Publicado: (2022) -
Prediction of clinically relevant postoperative pancreatic fistula using radiomic features and preoperative data
por: Bhasker, Nithya, et al.
Publicado: (2023) -
Comprehensive Analysis of Tumour Sub-Volumes for Radiomic Risk Modelling in Locally Advanced HNSCC
por: Leger, Stefan, et al.
Publicado: (2020)