Biomedical subjects

P E Lindsay

Publications and source records attributed to P E Lindsay.

3 recordsLinked to original sources

PubMed2006

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

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.

Computer Simulation↗

PubMed2005

A comparison of Monte Carlo dose calculation denoising techniques.

Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean-median-hybrid (CAMH) filtering, locally adaptive Savitzky-Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2-4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate results.

Algorithms↗

PubMed2003

The influence of brachytherapy dose heterogeneity on estimates of alpha/beta for prostate cancer.

The sensitivity of estimates of alpha/beta for prostate tumours to dose heterogeneity in 125I brachytherapy implants, as well as to variation in selected radiobiological parameters, is analysed. The tumour control probabilities of brachytherapy and external beam radiotherapy are equated for ranges of alpha, Tpot, RBE and external beam dose. For each combination of parameters, the equality is used to derive the value of alpha/beta. Different clinical (non-uniform) brachytherapy dose distributions, and three uniform brachytherapy dose distributions (120, 144 and 160 Gy) are used. For 'nominal' input parameter values of Tpot = 45 days, alpha = 0.2 Gy(-1), RBE = 1.4, and an external beam dose of 70 Gy, the values obtained for alpha/beta ranged between 2.1 and 12.3 Gy for all of the clinical DVHs, between 2.1 and 3.8 Gy for the better quality clinical implants and between 1.0 and 1.8 Gy for the uniform brachytherapy doses. When only 2% of the volume receiving the lowest dose is omitted from the clinical DVHs, the estimated alpha/beta values ranged between 1.4 and 2.1 Gy. When ranges of input parameters were also considered, the overall range of alpha/beta values for the clinical brachytherapy dose distributions lay between 1.1 and 12.3 Gy for the three best clinical implants, and between 0.7 and 6.3 Gy for uniform doses. We conclude that estimation of alpha/beta without taking into account dose heterogeneity and inter-patient variation may underestimate the actual value alpha/beta.

Brachytherapy↗