Prediction of real-world progression free survival (rwPFS) using a multimodal machine learning (ML) model for patients with HR+HER2-metastatic breast cancer (mBC) undergoing first line (1L) treatment with cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) and endocrine therapy (ET) Meeting Abstract

  

Authors:	Razavi, P.; An, J. A. R.; Erazo, T.; Schwartz, P.; Ferrer, L.; Gallinato, O.; Papillon, L.; Colin, T.; Bossy, M.; Andre, R.; Davies, J.; Falato, C.; Rashid, A.; Sreenivasan, S.; Decque, A.; Remorino, A.; Dellamonica, D.; Menu, P.
Abstract Title:	Prediction of real-world progression free survival (rwPFS) using a multimodal machine learning (ML) model for patients with HR+HER2-metastatic breast cancer (mBC) undergoing first line (1L) treatment with cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) and endocrine therapy (ET)
Meeting Title:	2025 ASCO Annual Meeting
Journal Title:	Journal of Clinical Oncology
Volume:	43
Issue:	16 Suppl.
Meeting Dates:	2025 May 30-Jun 3
Meeting Location:	Chicago, IL
ISSN:	0732-183X
Publisher:	American Society of Clinical Oncology
Date Published:	2025-06-01
Start Page:	e13088
Language:	English
ACCESSION:	WOS:001509262900001
DOI:	10.1200/JCO.2025.43.16_suppl.e13088
PROVIDER:	wos
Notes:	Source: Wos

https://synapse.mskcc.org/synapse/works/has_related_data_chk/227095