Open AccessPredicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records
Author(s) -
Zhanglin Lin Cui,
Zbigniew Kadziola,
Ilya Lipkovich,
Douglas E. Faries,
Kristin M. Sheffield,
Gebra Cuyún Carter
Publication year - 2021
Publication title -
journal of comparative effectiveness research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.567
H-Index - 23
eISSN - 2042-6313
pISSN - 2042-6305
DOI - 10.2217/cer-2020-0230
Subject(s) - medicine , discontinuation , regimen , hazard ratio , metastatic breast cancer , oncology , breast cancer , cancer , confidence interval
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 = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.