Literature DB >> 17068361

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

I El Naqa1, G Suneja, P E Lindsay, A J Hope, J R Alaly, M Vicic, J D Bradley, A Apte, J O Deasy.   

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.

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Year:  2006        PMID: 17068361     DOI: 10.1088/0031-9155/51/22/001

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  39 in total

1.  Heart irradiation as a risk factor for radiation pneumonitis.

Authors:  Ellen X Huang; Andrew J Hope; Patricia E Lindsay; Marco Trovo; Issam El Naqa; Joseph O Deasy; Jeffrey D Bradley
Journal:  Acta Oncol       Date:  2010-09-28       Impact factor: 4.089

2.  Dosimetric Predictors of Radiation-Induced Vaginal Stenosis After Pelvic Radiation Therapy for Rectal and Anal Cancer.

Authors:  Christina H Son; Ethel Law; Jung Hun Oh; Aditya P Apte; T Jonathan Yang; Elyn Riedel; Abraham J Wu; Joseph O Deasy; Karyn A Goodman
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-04-28       Impact factor: 7.038

3.  A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data.

Authors:  Jung Hun Oh; Jeffrey M Craft; Reid Townsend; Joseph O Deasy; Jeffrey D Bradley; Issam El Naqa
Journal:  J Proteome Res       Date:  2011-02-16       Impact factor: 4.466

4.  AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

Authors:  Isabella Castiglioni; Francesca Gallivanone; Paolo Soda; Michele Avanzo; Joseph Stancanello; Marco Aiello; Matteo Interlenghi; Marco Salvatore
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-11       Impact factor: 9.236

5.  A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy.

Authors:  J Ramos-Méndez; J Perl; J Schümann; J Shin; H Paganetti; B Faddegon
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

6.  Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration.

Authors:  A Wentzel; P Hanula; T Luciani; B Elgohari; H Elhalawani; G Canahuate; D Vock; C D Fuller; G E Marai
Journal:  IEEE Trans Vis Comput Graph       Date:  2019-08-22       Impact factor: 4.579

Review 7.  Radiomics in precision medicine for lung cancer.

Authors:  Julie Constanzo; Lise Wei; Huan-Hsin Tseng; Issam El Naqa
Journal:  Transl Lung Cancer Res       Date:  2017-12

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

Authors:  Olivier Gayou; Shiva K Das; Su-Min Zhou; Lawrence B Marks; David S Parda; Moyed Miften
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

9.  Dose/volume-response relations for rectal morbidity using planned and simulated motion-inclusive dose distributions.

Authors:  Maria Thor; Aditya Apte; Joseph O Deasy; Àsa Karlsdóttir; Vitali Moiseenko; Mitchell Liu; Ludvig Paul Muren
Journal:  Radiother Oncol       Date:  2013-11-11       Impact factor: 6.280

10.  Statistical simulations to estimate motion-inclusive dose-volume histograms for prediction of rectal morbidity following radiotherapy.

Authors:  Maria Thor; Aditya Apte; Joseph O Deasy; Ludvig Paul Muren
Journal:  Acta Oncol       Date:  2012-12-04       Impact factor: 4.089

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