rtdsm

rtdsm generates dose-surface maps (DSMs) from DICOM radiotherapy data to enable spatial analysis of dose distributions for hollow organs in dose–outcome studies.


Key Features:

  • DSM calculation: Computes DSMs from DICOM RT data to represent spatial dose distributions on hollow-organ surfaces.
  • User customizability: Provides configurable parameters to adjust DSM calculations and analysis settings for specific study requirements.
  • Equivalent dose conversions: Performs conversions between dose metrics to enable comparisons across different treatment plans or studies.
  • DSM feature extraction: Extracts common DSM-derived features used to quantify spatial dose patterns and identify radiosensitive subregions.
  • DSM accumulation: Accumulates DSMs to support longitudinal analyses and comparative studies.
  • DSM slicing methods: Implements planar and noncoplanar slicing approaches to generate alternative DSM representations.

Scientific Applications:

  • Dose–outcome studies: Enables analysis of spatial dose distributions when correlating delivered dose with clinical outcomes.
  • Radiosensitive subregion identification: Supports detection of spatially localized regions of sensitivity within hollow organs using DSM features.
  • Inter-treatment comparison: Facilitates comparison of DSMs across different treatment plans or modalities via equivalent dose conversions and DSM metrics.
  • Longitudinal and comparative analyses: Allows accumulation and comparison of DSMs over time or between cohorts for longitudinal studies.

Methodology:

Implementation and testing of two DSM slicing methods—planar and noncoplanar—to assess their effects on output DSMs.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
10/9/2022
Last Updated:
11/24/2024

Operations

Publications

Patrick HM, Kildea J. Technical note: <i>rtdsm</i>—An open‐source software for radiotherapy dose‐surface map generation and analysis. Medical Physics. 2022;49(11):7327-7335. doi:10.1002/mp.15900. PMID:35912447.