1.
Mol Cancer Res
; 22(4): 347-359, 2024 04 02.
Artigo
em Inglês
| MEDLINE
| ID: mdl-38284821
RESUMO
IMPLICATIONS: Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.