Phys Med. 2025 Nov 15;140:105215. doi: 10.1016/j.ejmp.2025.105215. Online ahead of print.
ABSTRACT
PURPOSE: Converting radiotherapy (RT) data from DICOM-RT into datasets suitable for statistical modelling remains challenging. We developed the DICOM Extraction and Structuration Toolkit (DEST), an automated solution that streamlines data extraction and ensures compatibility with statistical software. The reliability of DEST was also assessed by comparing its outputs with those from treatment planning systems (TPS) in study of cardiopulmonary dose-volume histograms (DVH) after radiotherapy (RT) for localised breast cancer.
METHODS: DEST comprises two main modules: a data extraction module and a viewer/analysis module. It processes DICOM-RT objects, including RT structure sets, RT plans, and RT dose files. Extractions are performed per treatment for predefined patient lists, after which structured data is consolidated. A 3D visualisation module verifies dose distributions for selected regions of interest, ensuring consistency and accuracy.
RESULTS: DEST was successfully applied to 404 patients from the “CANcer TOxicities – Radiation Therapy” (CANTO-RT) cohort. In this initial implementation, DEST showed strong overall agreement with regard to TPS for heart and lung dose metrics, including mean doses and dose-volume measurements. Specifically, near-minimum dose, median dose, near-maximum dose and percentages of volume receiving at least 10 Gy (V10Gy), 20 Gy (V20Gy), 30 Gy (V30Gy) and 40 Gy (V40Gy) showed high consistency between DEST and TPS.
CONCLUSIONS: DEST enhances accessibility to dose-volume metrics and will facilitate advanced modelling of medical outcomes (efficiency and risk) at the voxel level. By providing streamlined access to voxel spatial coordinates and local dose information, DEST enables more sophisticated analyses, such as clustering and localized region selection, supporting deeper insights into dose-response relationships.
PMID:41241989 | DOI:10.1016/j.ejmp.2025.105215
