Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose–volume outcome relationships

Abstract

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

DVH Analytics: A DVH database for clinicians and researchers

“…Furthermore, CERR is capable of working with patient files from DICOM or AAPM/RTOG archives, which makes data transfer between multiple platforms straightforward 3. For analysis across multiple patients, the Matlab‐based software DREES (dose–response explorer system) is an open‐source extension of CERR which provides a data‐driven analysis of treatment outcomes; DREES provides analytical tools such as fitting tumor control probability (TCP) and normal tissue complication probability (NTCP) curves, modeling and visualizing dose‐volume and plan metrics, and estimating uncertainty in planning parameters 4. The input data for DREES assume a Matlab‐based data structure comprising DVH and outcome data, which can be a limitation when working directly with DICOM files.…”

The Statistical Models Project (SMp) for Evaluations of Biological Radiation Effects

“…1 (7) and Eq, (8), and the own definitions and characteristics of its radiobiological components, and we can demonstrate why SL is a stochastic effect SMp type P2. For example, taking the habitually used S, which is a mix of U and SL (S(d)=U(d)+SL(d)) you can define the two deterministic regions for SL=0%, where S=100% and U=100%; and S=0% and U=0%.…”

“…The LQ S(n,d) and its derivations, like the BED, are widely used in the conventional radiotherapy [3][4][5][6][7][8][9][10][11], and in the proton therapy [12,13,14].…”

A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes

“…The same data were also analyzed using the DREES software, 10 which utilizes a sequential forward algorithm for variable selection, 12 along with a leave-one-out crossvalidation technique. At each model order, models are ranked according to the frequency in which they were selected as the best model in the cross-validation process.…”

“…Software tools were developed to address this issue, using, for example, self-organizing maps, 6 support vector machine algorithms, 7 neural networks, 8 or decision trees. 9 Two of these tools, DREES 10 and EUCLID, 11 which are MATLAB-based programs ͑The MathWorks Inc., Natick, MA͒, use a logistic regression model to correlate treatment outcomes with clinical factors. A key element in building such a model is the ability to select the variables that yield a high predictive power and statistically significant correlations between clinical factors and outcome.…”