ESTRO 2023 - Abstract Book

S1407

Digital Posters

ESTRO 2023

Conclusion The results of our study have shown that the tumor stress derived from BM with the longitudinal CT between pre-RT and mid-RT is a significant prognostic factor in patients with head and neck SCC underwent definitive chemoradiotherapy and correlated with the tumor progression better than the patient characteristics such as tumor staging, and TVRR.

PO-1696 Pydicer: An open-source tool for conversion and analysis of radiotherapy imaging data

P. Chlap 1 , D. Al Mouiee 1 , S. Deshpande 1 , R. Finnegan 2 , J. Cui 1 , V. Chin 1 , L. Holloway 1

1 UNSW, Ingham Institute & Liverpool and Macarthur Cancer Therapy Centres, Medical Physics, Liverpool, Australia; 2 University of Sydney, Ingham Institute & Liverpool and Northern Sydney Cancer Centre, Medical Physics, Sydney, Australia Purpose or Objective In many research projects that utilise radiotherapy imaging data (including structure sets and dose grids), a common first step is to prepare this data by converting it from DICOM into a suitable form for medical image analysis research. The NIfTI (Neuroimaging Informatics Technology Initiative) format is popular within the community and supported by many open source Python libraries, including SimpleITK, PyRadiomics and nnUNet. We developed an open-source tool that solves many steps commonly undertaken within radiotherapy research projects to convert and analyse imaging data. github.com/AustralianCancerDataNetwork/pydicer) is implemented using Python where several steps commonly undertaken in radiotherapy imaging research have been implemented that can be combined to produce a suitable pipeline for a given research project (figure 1). A flexible input module provides functionality to fetch DICOM data from various common DICOM data sources. Additional modules deal with linking the DICOM objects (Preprocess), converting these to NIfTI format (Convert), cleaning up data into subsets (Prepare), producing additional data objects (Generate), visualising cross-sections (Visualise) and finally computing radiomic features or dose metrics (Analyse). All converted data is stored within a well-defined directory structure with each object (image, structure set, dose, etc) saved to its own directory. This provides consistency with the DICOM standard to handle any dataset as well as flexibility Materials and Methods The Pydicer (PYthon Dicom Image ConvertER) tool (available open-source on GitHub: