Cargando…
Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts
BACKGROUND: Hybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to inves...
Autores principales: | Grahovac, M., Spielvogel, C. P., Krajnc, D., Ecsedi, B., Traub-Weidinger, T., Rasul, S., Kluge, K., Zhao, M., Li, X., Hacker, M., Haug, A., Papp, Laszlo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119059/ https://www.ncbi.nlm.nih.gov/pubmed/36738311 http://dx.doi.org/10.1007/s00259-023-06127-1 |
Ejemplares similares
-
Automated data preparation for in vivo tumor characterization with machine learning
por: Krajnc, Denis, et al.
Publicado: (2022) -
Sex-specific radiomic features of L-[S-methyl-(11)C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability
por: Papp, Laszlo, et al.
Publicado: (2023) -
Error mitigation enables PET radiomic cancer characterization on quantum computers
por: Moradi, S., et al.
Publicado: (2023) -
Breast Tumor Characterization Using [(18)F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics
por: Krajnc, Denis, et al.
Publicado: (2021) -
Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [(68)Ga]Ga-PSMA-11 PET/MRI
por: Papp, L., et al.
Publicado: (2020)