Personalized Prognostic Prediction Models for Breast Cancer Recurrence and Survival Incorporating Multidimensional Data.

ABSTRACT:

Background

In this study, we developed integrative, personalized prognostic models for breast cancer recurrence and overall survival (OS) that consider receptor subtypes, epidemiological data, quality of life (QoL), and treatment.

Methods

A total of 15 314 women with stage I to III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1997 and 2012 were used to generate prognostic models by Cox regression analysis in a two-stage study. Model performance was assessed by calculating the area under the curve (AUC) and calibration analysis and compared with Nottingham Prognostic Index (NPI) and PREDICT.

Results

Host characteristics were assessed for 10 809 women as the discovery population (median follow-up = 6.09 years, 1144

SUBMITTER: Wu X 

PROVIDER: S-EPMC6279311 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature