Purpose We quantitatively analyzed the characteristics of cone-beam computed tomography
(CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel …

Purpose Our purpose was to construct a computed tomography (CT)–based delta-radiomics
nomogram and corresponding risk classification system for individualized and accurate …

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 …

Purpose The aim was to investigate the advantages of dosiomic and radiomic features over
traditional dose-volume histogram (DVH) features for predicting the development of …

Purpose We initially aimed to ascertain the application value of inflammatory indexes in
predicting severe acute radiation pneumonitis (SARP). Furthermore, a novel nomogram and …

Purpose The study aimed to predict acute radiation esophagitis (ARE) with grade≥ 2 for
patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation …

Purpose This study aimed to establish machine learning models using dosimetric factors
and radiomics features within 5 regions of interest (ROIs) in treatment planning computed …

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 …

Background and purpose To establish and validate a contrast-enhanced computed
tomography-based hybrid radiomics nomogram for prediction of local recurrence-free …

Objective: This study aimed to construct an optimal model to predict radiation pneumonia
(RP) after radiotherapy for esophageal cancer using unified fractional dosiomics and to …