Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records.
J Comp Eff Res
; 10(9): 777-795, 2021 06.
Article
em En
| MEDLINE
| ID: mdl-33980048
ABSTRACT
Aim:
To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods:
Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics.Results:
RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44â¼0.79).Conclusion:
RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Female
/
Humans
Idioma:
En
Revista:
J Comp Eff Res
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos