Welcome to the brand new DVH Analytics, rewritten as a native OS application with wxPython. This version is currently only available as source code during the public beta. Compiled versions will be available after successful testing. The previous web-based version can be found here but is no longer being developed.

Clone this project and install python dependencies.
Then call the following from the top-level project directory with python 3:

python dvha_app.py

For installation instructions of the source code, see our installation notes.

DVH Analytics is a software application to help radiation oncology departments build an in-house database of treatment planning data for the purpose of historical comparisons and statistical analysis.

The application builds a SQL database of DVHs and various planning parameters from DICOM files (i.e., Plan, Structure, Dose). Since the data is extracted directly from DICOM files, we intend to accommodate an array of treatment planning system vendors.

In addition to viewing DVH data, this software provides methods to:

• download queried data
• create time-series plots of various planning and dosimetric variables
• calculate correlations
• generate multi-variable linear and machine learning regressions.
• Additional screenshots available here.

The code is built upon these core libraries:

• wxPython Phoenix - Build a native GUI on Windows, Mac, or Unix systems
• Pydicom - Read, modify and write DICOM files with python code
• dicompyler-core - Extensible radiation therapy research platform and viewer for DICOM and DICOM RT
• Bokeh - Interactive Web Plotting for Python
• scikit-learn - Machine Learning in Python

DOI: https://doi.org/10.1002/acm2.12401
“DVH Analytics: A DVH Database for Clinicians and Researchers,” J. App. Clin. Med. Phys. - JACMP-2018-01083

• Python >=3.5
• wxPython Phoenix >= 4.0.4
• Pydicom >=1.0
• dicompyler-core 0.5.3
• Bokeh >= 1.2.0
• PostgreSQL and psycopg2
• SciPy
• NumPy
• Shapely
• Statsmodels 0.8.0
• Scikit-learn
• regressors