Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers

To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs between 2014...

Descripción completa

Detalles Bibliográficos
Autores principales: Veeraraghavan, Harini, Friedman, Claire F., DeLair, Deborah F., Ninčević, Josip, Himoto, Yuki, Bruni, Silvio G., Cappello, Giovanni, Petkovska, Iva, Nougaret, Stephanie, Nikolovski, Ines, Zehir, Ahmet, Abu-Rustum, Nadeem R., Aghajanian, Carol, Zamarin, Dmitriy, Cadoo, Karen A., Diaz, Luis A., Leitao, Mario M., Makker, Vicky, Soslow, Robert A., Mueller, Jennifer J., Weigelt, Britta, Lakhman, Yulia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575573/
https://www.ncbi.nlm.nih.gov/pubmed/33082371
http://dx.doi.org/10.1038/s41598-020-72475-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575573/
https://www.ncbi.nlm.nih.gov/pubmed/33082371
http://dx.doi.org/10.1038/s41598-020-72475-9

Ejemplares similares