TRIDENT: Machine learning (ML) multimodal signatures to identify patients that would benefit most from tremelimumab (T) addition to durvalumab (D) + chemotherapy (CT) with data from the POSEIDON trial Meeting Abstract
| Authors: | Skoulidis, F.; Jabbour, S.; Garon, E. B.; Iyengar, P.; Scagliotti, G.; Ferrer, L.; Etchepare, G.; Gallinato, O.; Faure, J.; Bernard, P.; Colin, T.; Menu, P.; Lin, Y.; Cai, L.; Faria, J.; Remorino, A.; Luciani-Silverman, L.; Miller, K. P.; Dellamonica, D.; Zhang, Y. |
| Abstract Title: | TRIDENT: Machine learning (ML) multimodal signatures to identify patients that would benefit most from tremelimumab (T) addition to durvalumab (D) + chemotherapy (CT) with data from the POSEIDON trial |
| Meeting Title: | ESMO Congress 2024 |
| Journal Title: | Annals of Oncology |
| Volume: | 35 |
| Issue: | Suppl. 2 |
| Meeting Dates: | 2024 Sep 13-17 |
| Meeting Location: | Barcelona, Spain |
| ISSN: | 0923-7534 |
| Publisher: | Oxford University Press |
| Date Published: | 2024-09-01 |
| Start Page: | S842 |
| End Page: | S843 |
| Language: | English |
| ACCESSION: | WOS:001326612901625 |
| DOI: | 10.1016/j.annonc.2024.08.1380 |
| PROVIDER: | wos |
| Notes: | Meeting Abstract: 1325P -- Source: Wos |
