Rapid patient-specific organ dose estimation in computed tomography scans via integration of radiomics features and neural networks.

ABSTRACT:

Background

Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features.

Methods

CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predict

SUBMITTER: Shao W 

PROVIDER: S-EPMC11485356 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature