Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

ABSTRACT:

Objective

We explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible.

Methods and materials

HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data.

Results

Among 391 patients identified, WL predict

SUBMITTER: Cheng Z 

PROVIDER: S-EPMC6127872 | biostudies-literature | 2018 Jul-Sep

REPOSITORIES: biostudies-literature