Cargando…
AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance
Purpose: Success of clinical trials increasingly relies on effective selection of the target patient populations. We hypothesize that computational analysis of pre-accrual imaging data can be used for patient enrichment to better identify patients who can potentially benefit from investigational age...
Autores principales: | Tomaszewski, Michal R., Fan, Shuxuan, Garcia, Alberto, Qi, Jin, Kim, Youngchul, Gatenby, Robert A., Schabath, Matthew B., Tap, William D., Reinke, Denise K., Makanji, Rikesh J., Reed, Damon R., Gillies, Robert J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8880510/ https://www.ncbi.nlm.nih.gov/pubmed/35202193 http://dx.doi.org/10.3390/tomography8010028 |
Ejemplares similares
-
An evolutionary framework for treating pediatric sarcomas
por: Reed, Damon R., et al.
Publicado: (2020) -
Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening
por: Pérez-Morales, Jaileene, et al.
Publicado: (2020) -
Recurrent parachordoma of the lower back: A case report
por: Belzarena, Ana C., et al.
Publicado: (2018) -
Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients
por: Tunali, Ilke, et al.
Publicado: (2017) -
Retropubic parasymphyseal cyst: A rare entity
por: Ahmed, Abraham, et al.
Publicado: (2020)