Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis.

ABSTRACT:

Objective

The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics.

Materials and methods

The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD₂) model was used to convert the physical dose to th

SUBMITTER: Zhou L 

PROVIDER: S-EPMC9966572 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature