The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): a population based study from the SEER database.

ABSTRACT:

Purpose

We aimed to analysis the impact of chemotherapy and establish prediction models of prognosis in early elderly triple negative breast cancer (eTNBC) by using machine learning.

Methods

We enrolled 4,696 patients in SEER Database who were 70 years or older, diagnosed with primary early TNBC(larger than 5 mm), from 2010 to 2016. The propensity-score matched method was utilized to reduce covariable imbalance. Univariable and multivariable analyses were used to compare breast cancer-specific survival(BCSS) and overall survival(OS). Nine models were developed by machine learning to predict the 5-year OS and BCSS for patients received chemotherapy.

Results

Compared to matched patients in no-chemotherapy group, multivariate analysis showed a better survival in chemothe

SUBMITTER: Huang K 

PROVIDER: S-EPMC8973884 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature