INVESTIGADORES
LUNA Daniel Roberto
artículos
Título:
1174P Preliminary prediction of EGFR-mutant non-small cell lung cancer outcome using radiomic signature
Autor/es:
MINATTA, J.N.; MOSQUERA, C.; AINESEDER, M.; NUÑEZ, M.A. MESTAS; DEZA, D.; LUPINACCI, L.; BASBUS, L.; BENITEZ, S.E.; SEEHAUS, A.; LUNA, D.R.; BERESÑAK, A.D.; DIAZ, F.N.
Revista:
ANNALS OF ONCOLOGY
Editorial:
OXFORD UNIV PRESS
Referencias:
Año: 2021 vol. 32 p. 941 - 942
ISSN:
0923-7534
Resumen:
Although osimertinib is currently the standard of care, cost-effectivenesscould be improved by identifying a subset of patients who will present longer progression-free survival (PFS) with other more accessible treatments. We propose anon-invasive approach based on radiomics and machine learning to identify thesepatients? risk of progression.
