Radiation pneumonia predictive model for radiotherapy in esophageal carcinoma patients.

ABSTRACT:

Background

The machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.

Methods

A total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system,

SUBMITTER: Sheng L 

PROVIDER: S-EPMC10580570 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature