Open AccessDVH Analytics: A DVH database for clinicians and researchers
Author(s) -
Cutright Dan,
Gopalakrishnan Mahesh,
Roy Arkajyoti,
Panchal Aditya,
Mittal Bharat B.
Publication year - 2018
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.12401
Subject(s) - dicom , computer science , software , database , analytics , python (programming language) , data mining , operating system
In this study, we build a vendor‐agnostic software application capable of importing and analyzing non‐image‐based DICOM files for various radiation treatment modalities (i.e., DICOM RT Dose, RT Structure, and RT Plan files). Dose‐volume histogram ( DVH ) and planning data are imported into a SQL database, and methods are provided to manage, edit, view, and download data. Furthermore, the software provides various analytical tools for plan evaluations, plan comparisons, benchmarking, and plan outcome predictions. DVH Analytics is developed using Python, including libraries such as pydicom, dicompyler, psycopg2, SciPy, Statsmodels, and Bokeh for parsing DICOM files, computing DVH s, communicating with a Postgre SQL database, performing statistical analyses, and creating a web‐based user interface. This software is open‐source and compatible with Windows, Mac OS , and Linux. For proof‐of‐concept, a database with over 3,000 DVH s from a single physician's head & neck practice was built. From these data, differences in means, correlations, and temporal trends in dose to multiple organs‐at‐risk ( OAR s) were observed. Furthermore, an example of the predictive regression tool is reported, where a model was constructed to predict maximum dose to brainstem based on minimum distance from planning target volume ( PTV ) and treatment beam source‐to‐skin distance ( SSD ). With DVH Analytics, we have developed a free, open‐source software program to parse, organize, and analyze non‐image‐based DICOM data for use in a radiation oncology setting. Furthermore, this software can be used to generate statistical models for the purposes of quality control or outcome predictions and correlations.