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Joseph O Deasy

Publications and source records attributed to Joseph O Deasy.

At least 19 recordsLinked to original sources
PubMed2026

Integrating Genomic and Nongenomic Data to Stratify the Risk of Contralateral Breast Cancer After Radiation Therapy.

PURPOSE: Women treated with radiation therapy (RT) for breast cancer have an increased risk of developing radiation-associated contralateral breast cancer (CBC). Predicting CBC events is challenging because of the complex interplay of genomic, treatment, personal, and clinical factors. This study investigated computational methods that integrate genome-wide single-nucleotide polymorphisms and nongenomic data to develop a risk stratification model for developing CBC in women treated with RT for their first primary breast cancer. METHODS AND MATERIALS: This study used a subset of the population-based Women's Environmental Cancer and Radiation Epidemiology study that included 633 CBC cases and 1253 individually matched unilateral breast cancer controls who were treated with RT and had single-nucleotide polymorphism data available from a genome-wide association study. The study population was split into training, validation, and test sets for rigorous modeling and validation. Three data integration methods were compared in terms of their ability to stratify CBC risk: (1) naive integration; (2) sequential integration; and (3) sequential iterative integration. A biological analysis of the final model was performed using gene set enrichment analysis and protein-protein interaction analysis with gene annotation information informed by the model. RESULTS: The best-performing integration method was the sequential iterative integration equipped with the mixed-effect random forest algorithm. This approach achieved an area under the curve of 0.64 to stratify CBC risk in the test set, representing moderate predictive power. Calibration analysis showed good agreement between the lowest and highest risk bins stratified using sorted predicted values in the test set, resulting in an odds ratio of 3.27 for both predicted and observed CBC occurrence. Gene set enrichment analysis and protein-protein interaction analysis revealed that genes with high importance scores were associated with pathways relevant to lipid and fatty acid metabolism as well as breast cancer sensitivity to tamoxifen. CONCLUSIONS: The mixed-effect random forest approach demonstrated the potential for integrating high-dimensional genomic and low-dimensional nongenomic data to stratify CBC risk.

Humans↗
PubMed2025

ORCO: Ollivier-Ricci Curvature-Omics-an unsupervised method for analyzing robustness in biological systems.

MOTIVATION: Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret large-scale omic data are still needed. RESULTS: To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature for omics (ORCO). ORCO incorporates omics data and a network describing biological relationships between the genes or proteins and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC is an edge-based measure that assesses network robustness. It captures functional cooperation in gene signaling using a consistent information-passing measure, which can help investigators identify therapeutic targets and key regulatory modules in biological systems. ORC has identified novel insights in multiple cancer types using genomic data and in neurodevelopmental disorders using brain imaging data. This tool is applicable to any data that can be represented as a network. AVAILABILITY AND IMPLEMENTATION: ORCO is an open-source Python package and is publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

Software↗
PubMed2006

Optimization of the temporal pattern of radiation: an IMRT based study.

PURPOSE: To investigate how the temporal pattern of dose applied during a single-intensity modulated radiation therapy (IMRT) fraction can be arranged to maximize or minimize cell kill. METHODS AND MATERIALS: Using the linear-quadratic repair-time model and a simplified IMRT delivery pattern model, the surviving fraction of cells for a single fraction was calculated for all permutations of the dose delivery pattern for an array of clinically based IMRT cases. Maximization of cell kill was achieved by concentrating the highest doses in the middle of a fraction, while minimization was achieved by spreading the highest doses between the beginning and end. The percent difference between maximum and minimum cell kill (%Diff(min/max)) and the difference between maximum and minimum total doses normalized to 2 Gy/fx (deltaNTD(2 Gy)) was calculated for varying fraction durations (T), alpha/beta ratios, and doses/fx. RESULTS: %Diff(min/max) and deltaNTD(2 Gy) both increased with increasing T and with decreasing alpha/beta. The largest increases occurred with dose/fx. With alpha/beta = 3 Gy and 30 min/fx, %Diff(min/max) ranged from 2.7-5.3% for 2 Gy/fx to 48.6-74.1% for 10 Gy/fx, whereas deltaNTD(2 Gy) ranged from 1.2 Gy-2.4 Gy for 30 fractions of 2 Gy/fx to 2.3-4.8 Gy for 2 fractions of 10.84 Gy/fx. Using alpha/beta = 1.5 Gy, an analysis of prostate hypofractionation schemes yielded differences in clinical outcome based on the pattern of applied dose ranging from 3.2%-6.1% of the treated population. CONCLUSIONS: Rearrangement of the temporal pattern of dose for a single IMRT fraction could be used to optimize cell kill and to directly, though modestly, affect treatment outcome.

Algorithms↗
PubMed2006

A prospective study of differences in duodenum compared to remaining small bowel motion between radiation treatments: implications for radiation dose escalation in carcinoma of the pancreas.

PURPOSE: As a foundation for a dose escalation trial, we sought to characterize duodenal and non-duodenal small bowel organ motion between fractions of pancreatic radiation therapy. PATIENTS AND METHODS: Nine patients (4 women, 5 men) undergoing radiation therapy were enrolled in this prospective study. The patients had up to four weekly CT scans performed during their course of radiation therapy. Pancreas, duodenum and non-duodenal small bowel were then contoured for each CT scan. On the initial scan, a four-field plan was generated to fully cover the pancreas. This plan was registered to each subsequent CT scan. Dose-volume histogram (DVH) analyses were performed for the duodenum, non-duodenal small bowel, large bowel, and pancreas. RESULTS: With significant individual variation, the volume of duodenum receiving at least 80% of the prescribed dose was consistently greater than the remaining small bowel. In the patient with the largest inter-fraction variation, the fractional volume of non-duodenal small bowel irradiated to at least the 80% isodose line ranged from 1% to 20%. In the patient with the largest inter-fraction variation, the fractional volume of duodenum irradiated to at least the 80% isodose line ranged from 30% to 100%. CONCLUSION: The volume of small bowel irradiated during four-field pancreatic radiation therapy changes substantially between fractions. This suggests dose escalation may be possible. However, dose limits to the duodenum should be stricter than for other segments of small bowel.

Dose-Response Relationship, Radiation↗
PubMed2006

Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters.

PURPOSE: To determine the clinical, dosimetric, and spatial parameters that correlate with radiation pneumonitis. METHODS AND MATERIALS: Patients treated with high-dose radiation for non-small-cell lung cancer with three-dimensional treatment planning were reviewed for clinical information and radiation pneumonitis (RP) events. Three-dimensional treatment plans for 219 eligible patients were recovered. Treatment plan information, including parameters defining tumor position and dose-volume parameters, was extracted from non-heterogeneity-corrected dose distributions. Correlation to RP events was assessed by Spearman's rank correlation coefficient (R). Mathematical models were generated that correlate with RP. RESULTS: Of 219 patients, 52 required treatment for RP (median interval, 142 days). Tumor location was the most highly correlated parameter on univariate analysis (R = 0.24). Multiple dose-volume parameters were correlated with RP. Models most frequently selected by bootstrap resampling included tumor position, maximum dose, and D35 (minimum dose to the 35% volume receiving the highest doses) (R = 0.28). The most frequently selected two- or three-parameter models outperformed commonly used metrics, including V20 (fractional volume of normal lung receiving >20 Gy) and mean lung dose (R = 0.18). CONCLUSIONS: Inferior tumor position was highly correlated with pneumonitis events within our population. Models that account for inferior tumor position and dosimetric information, including both high- and low-dose regions (D(35), International Commission on Radiation Units and Measurements maximum dose), risk-stratify patients more accurately than any single dosimetric or clinical parameter.

Adult↗
PubMed2006

Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors.

PURPOSE: The probability of a specific radiotherapy outcome is typically a complex, unknown function of dosimetric and clinical factors. Current models are usually oversimplified. We describe alternative methods for building multivariable dose-response models. METHODS: Representative data sets of esophagitis and xerostomia are used. We use a logistic regression framework to approximate the treatment-response function. Bootstrap replications are performed to explore variable selection stability. To guard against under/overfitting, we compare several analytical and data-driven methods for model-order estimation. Spearman's coefficient is used to evaluate performance robustness. Novel graphical displays of variable cross correlations and bootstrap selection are demonstrated. RESULTS: Bootstrap variable selection techniques improve model building by reducing sample size effects and unveiling variable cross correlations. Inference by resampling and Bayesian approaches produced generally consistent guidance for model order estimation. The optimal esophagitis model consisted of 5 dosimetric/clinical variables. Although the xerostomia model could be improved by combining clinical and dose-volume factors, the improvement would be small. CONCLUSIONS: Prediction of treatment response can be improved by mixing clinical and dose-volume factors. Graphical tools can mitigate the inherent complexity of multivariable modeling. Bootstrap-based variable selection analysis increases the reliability of reported models. Statistical inference methods combined with Spearman's coefficient provide an efficient approach to estimating optimal model order.

Carcinoma, Non-Small-Cell Lung↗
PubMed2006

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods.

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.

Algorithms↗
PubMed2006

Progress toward a microradiation therapy small animal conformal irradiator.

Microradiation therapy (microRT) systems are being designed to provide conformal radiation therapy to small animals enabling quantitative radiation response evaluation. We used a Monte Carlo approach to estimate the radiation dose distributions from proposed blueprints and developed a beam model to aid in the microRT system design process. This process was applied to a prototype irradiator that uses a small (3 mm long and 3 mm in diameter), cylindrical, high-activity 192Ir source delivering the radiation beam using custom-fabricated tungsten collimators. The BEAMnrc Monte Carlo code was used to simulate dose distributions from these prototype collimators. Simulations were performed at three source-to-surface distances (50, 60, and 70 mm), and with five circular field sizes (5, 7.5, 10, 12.5, and 15 mm). A dose to a 50 X 50 X 50 mm3 water phantom with 1 X 1 X 1 mm3 voxel spacing was computed. A multiparameter dose calculation algorithm was developed to efficiently and accurately calculate doses for treatment planning exercises. The parametrization was selected so that the parameters varied smoothly as a function of depth, source-to-surface distance, and field size, allowing interpolation for geometries that were not simulated using the Monte Carlo simulation. Direct comparison of the model with the Monte Carlo simulations showed that the variations were within 5% error for field sizes larger than 10 mm, and up to 10% for smaller field sizes.

Animals↗
PubMed2005

Dose-volume modeling of salivary function in patients with head-and-neck cancer receiving radiotherapy.

PURPOSE: We investigated the factors that affect salivary function after head-and-neck radiotherapy (RT), including parotid gland dose-volume effects, potential compensation by less-irradiated gland tissue, and functional recovery over time. METHODS AND MATERIALS: Sixty-five patients with head-and-neck tumors were enrolled in a prospective salivary function study. RT was delivered using intensity-modulated RT (n = 45), forward-planning three-dimensional conformal RT (n = 14), or three-dimensional conformal RT with an intensity-modulated RT boost (n = 6). Whole salivary flow was measured before therapy and at 6 months (n = 61) and 12 months (n = 31) after RT. A wide variety of dose-volume models to predict post-RT salivary function were tested. Xerostomia was defined according to the subjective, objective, management, analytic (SOMA) criteria as occurring when posttreatment salivary function was < 25% of the pretreatment function. Multivariate logistic regression analysis was used to assess the combined effect of dose-volume, patient-, and treatment-related factors. RESULTS: A significant correlation was observed between the relative quality-of-life scores and relative stimulated saliva values at 6 months after RT (Spearman's correlation coefficient [R(s)] = 0.46, p < 0.001). The dose-volume factors were by far the strongest correlates with stimulated saliva flow, although other factors showed modest significance in multimetric models (chemotherapy, gender, and Karnofsky performance status). Several fitted dose-volume models provided a good mathematical description of the data. Significant noise in the salivary measurements (repeated measurement coefficient of variation was 27% in normal subjects) precluded selection of any one of the models presented solely on the basis of the objective fit criteria. Nevertheless, the mean dose-exponential model, in which each parotid gland's relative salivary gland function equaled exp(-A x mean gland dose), with A equal to 0.054/Gy (68% confidence interval 0.052-0.059), provided a good representation of the data and was incorporated into our multimetric analysis. Using that model, we estimated that a mean parotid dose of 25.8 Gy, on average, was likely to reduce a single parotid gland's flow to 25% of its pretreatment value, regardless of the treatment delivery method. Significant correlations were observed between a logistic multivariate model (incorporating the mean dose-exponential equation, gender, and Karnofsky performance status) and stimulated saliva flow at 6 months (R(s) = 0.73), stimulated saliva flow at 12 months (R(s) = 0.54), and quality-of-life score at 6 months (R(s) = 0.35) after RT. CONCLUSION: Stimulated parotid salivary gland dose-volume models strongly correlated with both stimulated salivary function and quality-of-life scores at 6 months after RT. The mean stimulated saliva flow rates improved from 6 to 12 months after RT. Salivary function, in each gland, appeared to be lost exponentially at a rate of approximately 5%/1 Gy of mean dose. Additional research is necessary to distinguish among the models for use in treatment planning. The incidence of xerostomia was significantly decreased when the mean dose of at least one parotid gland was kept to < 25.8 Gy with conventional fractionation. However, even lower mean doses imply increased late salivary function.

Adult↗
PubMed2004

Intensity-modulated radiation therapy for oropharyngeal carcinoma: impact of tumor volume.

PURPOSE: To assess the therapeutic outcomes in oropharyngeal cancer patients treated with intensity-modulated radiotherapy (IMRT) and analyze the impact of primary gross tumor volume (GTV) and nodal GTV (nGTV) on survival and locoregional control rates. METHODS AND MATERIALS: Between February 1997 and September 2001, 74 patients with squamous cell carcinoma of the oropharynx were treated with IMRT. Thirty-one patients received definitive IMRT; 17 also received platinum-based chemotherapy. Forty-three patients received combined surgery and postoperative IMRT. The median follow-up for all patients was 33 months (range, 9-60 months). Fifty-two patients (70.3%) had Stage IV disease, 17 patients (23%) had Stage III, 3 patients (4.1%) had Stage II, and 2 patients (2.7%) had Stage I tumors. The mean prescription dose was 70 and 66 Gy, respectively, for the definitive and postoperative cohorts. The daily fraction dose was either 1.9 or 2 Gy, five times weekly. The GTV and/or nGTV were determined and derived using the Computational Environment for Radiotherapy Research, a free software package developed at Washington University. The mean GTV was 30.5 +/- 22.3 cm(3), and the mean nGTV was 23.2 +/- 20.6 cm(3). RESULTS: Ten locoregional failures were observed. Six patients died of disease and three died of concurrent disease. Distant metastasis developed in 6 patients. The 4-year estimate of overall survival was 87%, and the 4-year estimate of disease-free survival was 81% (66% in the definitive vs. 92% in the postoperative RT group). The 4-year estimate of locoregional control was 87% (78% in the definitive vs. 95% in the postoperative RT group); the 4-year estimate of distant metastasis-free survival was 90% (84% in the definitive vs. 94% in the postoperative group). Multivariate analysis showed that GTV and nGTV were independent risk factors determining locoregional control and disease-free survival for definitive oropharyngeal IMRT patients. The worst late toxicities documented were as follows: 32 patients with Grade 1 and 9 with Grade 2 xerostomia; 2 with Grade 1 and 1 with Grade 2 skin toxicity; 3 with Grade 1 late mucositis; and 3 with Grade 1 trismus. Seventeen patients required gastrostomy tube placement. CONCLUSION: IMRT is an effective treatment modality for locally advanced oropharyngeal carcinoma. The GTV and nGTV are the most important factors predictive of therapeutic outcome.

Adult↗
PubMed2004

Dosimetric correlates for acute esophagitis in patients treated with radiotherapy for lung carcinoma.

PURPOSE: Acute esophagitis is a common complication of radiotherapy (RT) for non-small-cell carcinoma of the lung. Previous reports have related esophagitis to dosimetric parameters such as the length of the irradiated esophagus, maximal dose, or volume of the organ treated beyond a threshold dose. However, when using oblique beams, a portion of the esophageal circumference may be outside the treated field, resulting in partial esophageal irradiation. Therefore, our aim was to determine whether the irradiated esophageal surface area and/or esophageal volume are predictive of acute esophagitis in relation to other clinical and treatment-related factors. METHODS AND MATERIALS: Complete dose-volume information was gathered for 166 patients receiving definitive RT for Stage I-IIIB non-small-cell carcinoma of the lung at our institution. Seventy-eight patients received chemotherapy (37 before RT and 41 concurrently). All patients were treated to doses of 60-74 Gy (median, 70 Gy) delivered in single daily fractions of 1.8-2.1 Gy. The doses were prescribed to the isocenter without using heterogeneity corrections; however, the doses were corrected to account for lung heterogeneity in this report. Esophageal contrast was used to contour the esophagus from the cricoid to the gastroesophageal junction in each case. Esophagitis was scored according to the Radiation Therapy Oncology Group criteria with Grade 2 or worse considered clinically significant. To determine the importance of the irradiated surface area, the volumetric treatment plan for each patient was prepared for analysis by relating a surface area to each point of the esophagus contour. Spearman's rank correlation was used to correlate the esophagitis score with A(d), where A represents the surface area (in centimeters squared) receiving the dose, d, or greater (in Gray), or V(d), where V represents the volume (in centimeters cubed) receiving d (in Gray). The surface areas studied were A(5)-A(80) or V(5)-V(80) in 5-Gy increments. The clinical parameters studied in univariate analysis included patient age, stage, performance status, use of pretreatment chemotherapy, and use of concurrent chemotherapy. Step-wise regression analysis was then used to determine the statistically significant factors predicting acute esophagitis. RESULTS: Forty-five patients (27%) developed Grade 2 or worse esophagitis, 37 developed Grade 2, 7 Grade 3, and 1 Grade 4. No deaths resulted from this complication. The most statistically significant single parameters for predicting acute esophagitis were A(55), V(60), and the use of concurrent chemotherapy. Age, stage, performance status, and pre-RT chemotherapy had no statistically significant influence on the incidence of acute esophagitis. On logistic regression analysis, A(55) (p < or =0.0005), V(60) (p < or =0.001), and the use of concurrent chemotherapy (p = 0.001) emerged as statistically significant correlates of acute esophagitis. CONCLUSION: The esophageal surface area receiving > or =55 Gy, the esophageal volume receiving > or =60 Gy, and the use of concurrent chemotherapy were the most statistically significant predictive factors for early esophagitis. Adequate dosimetric coverage of the planning target volume remains the goal of RT planning. High values of A(55) and/or V(60) are indicative of the development of acute esophagitis and may indicate a need to explore alternative RT planning options.

Acute Disease↗
PubMed2004

Beamlet dose distribution compression and reconstruction using wavelets for intensity modulated treatment planning.

Intensity modulated radiation therapy (IMRT) treatment planning is often formulated as the optimization of weights of fixed-geometry subfields (beamlets). Efficient optimization techniques can be based on direct storage of the influence matrix relating beamlet weights to dose values. However, direct storage of beamlet dose distributions for IMRT treatment planning can easily exceed several gigabytes, and is therefore often not feasible. We present a method for rapidly calculating full three-dimensional IMRT dose distributions, based on a vector of beamlet weights. The method is based on compressed beamlet dose distributions using fast digital wavelet transforms and so-called hard thresholding. We studied the method with a rectangular beamlet of 0.5 cm x 0.5 cm cross section from a monoenergetic 6 MeV photon point source simulated in homogeneous (water) and heterogeneous (CT-data) phantoms. Dose was calculated using the accurate VMC+ + Monte Carlo engine. The beamlet dose distributions were wavelet transformed and compressed by dropping wavelet coefficients below a given threshold value. Dose is then computed using the remaining wavelets. Selection of the wavelet basis function, decomposition level, and threshold values, for different slice orientations (transverse or parallel to the beam) and varying angles of beamlet incidence are studied. A typical in-slice compression ratio for a plane containing a beamlet was 32:1 using the sym2 wavelet and a threshold of 0.01, with a typical root-mean-square error, for voxels above 50% of the maximum dose, of about 0.04%. The overall compression performance, which includes many planes with little information content, is on the order of 100:1 or greater compared to full matrix storage. Although other methods are available to make the use of stored influence matrix values more feasible in IMRT treatment planning (such as using coarse grids or restricting values to defined volumes of interest) we conclude that wavelet compression facilitates the storage and use of full pencil dose deposition (influence matrix) data in IMRT treatment planning.

Algorithms↗
PubMed2004

A treatment planning study comparing HDR and AGIMRT for cervical cancer.

The customization of brachytherapy dose distributions for gynecologic malignancies is limited by the spatial positioning of the applicators. We tested the hypothesis that applicator-guided intensity modulated radiation therapy (AGIMRT) has the potential to deliver highly conformal dose distributions to cervical tumors, representing improvement over distributions obtained with intracavitary brachytherapy. A commercial three-dimensional (3-D) treatment planning system was used to create plans for ten cervical cancer patients treated at our institution. Dose distributions of conventionally designed high dose rate (HDR) plans were compared against those of AGIMRT. Tumor delineation was based on a previously published binary threshold technique, using image intensity on positron emission tomography (PET) scans. AGIMRT treatment schedules were designed using two fraction sizes: 6.5 Gy, to directly reproduce the HDR fractionation, and 1.8 Gy, to simulate traditional external beam fractionation. The average minimum tumor dose was significantly greater for the AGIMRT dose distributions than for the HDR distributions (64.2 Gy vs 33.6 Gy; p = 0.005). The mean percent tumor volume at the prescription dose was higher for the AGIMRT plans (90.0% vs 58.2%; p = 0.005). Using AGIMRT, the mean percent volume at the tolerance limit was decreased for the bladder (6.1% vs 16.6%; p = 0.047) but increased for the rectum (4.1% vs 2.2%; p = 0.646). Our study suggests that there may be conceptual and dosimetric advantages to replacing HDR with AGIMRT for patients with large-volume cervical tumors. This investigation is being expanded using sequential PET images to model tumor regression and compare brachytherapy and AGIMRT throughout the course of therapy.

Body Burden↗
PubMed2003

Absence of multiple local minima effects in intensity modulated optimization with dose-volume constraints.

This paper reports on the analysis of intensity modulated radiation treatment optimization problems in the presence of non-convex feasible parameter spaces caused by the specification of dose-volume constraints for the organs-at-risk (OARs). The main aim was to determine whether the presence of those non-convex spaces affects the optimization of clinical cases in any significant way. This was done in two phases: (1) Using a carefully designed two-dimensional mathematical phantom that exhibits two controllable minima and with randomly initialized beamlet weights, we developed a methodology for exploring the nature of the convergence characteristics of quadratic cost function optimizations (deterministic or stochastic). The methodology is based on observing the statistical behaviour of the residual cost at the end of optimizations in which the stopping criterion is progressively more demanding and carrying out those optimizations to very small error changes per iteration. (2) Seven clinical cases were then analysed with dose-volume constraints that are stronger than originally used in the clinic. The clinical cases are two prostate cases differently posed, a meningioma case, two head-and-neck cases, a spleen case and a spine case. Of the 14 different sets of optimizations (with and without the specification of maximum doses allowed for the OARs), 12 fail to show any effect due to the existence of non-convex feasible spaces. The remaining two sets of optimizations show evidence of multiple minima in the solutions, but those minima are very close to each other in cost and the resulting treatment plans are practically identical, as measured by the quality of the dose-volume histograms (DVHs). We discuss the differences between fluence maps resulting from those similar treatment plans. We provide a possible reason for the observed results and conclude that, although the study is necessarily limited, the annealing characteristics of a simulated annealing method may not be justified in clinical optimization in the presence of dose-volume constraints. The results of optimizations by the Newton gradient (NG) method with a quadratic cost function are reported in detail. An adaptive simulated annealing method, optimizing the same function, and the dynamically penalized likelihood method, optimizing a log likelihood function, have also been used in the study. The results of the latter two methods have only been discussed briefly, as they yielded the same conclusions as the NG method.

Algorithms↗
PubMed2003

CERR: a computational environment for radiotherapy research.

A software environment is described, called the computational environment for radiotherapy research (CERR, pronounced "sir"). CERR partially addresses four broad needs in treatment planning research: (a) it provides a convenient and powerful software environment to develop and prototype treatment planning concepts, (b) it serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.), (c) it provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and (d) it provides a convenient and powerful tool for sharing and reproducing treatment planning research results. The functional components currently being distributed, including source code, include: (1) an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell-array data object, facilitating manipulation; (2) viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash, (3) a suite of contouring tools to edit and/or create anatomical structures, (4) dose-volume and dose-surface histogram calculation and display tools, and (5) various predefined commands. CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self-describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose-volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization. In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.

Biomedical Research↗
PubMed2002

The generalized equivalent uniform dose function as a basis for intensity-modulated treatment planning.

The efficiency of intensity-modulated radiation therapy (IMRT) treatment planning depends critically on the presence or absence of multiple local minima in the feasible search space. We analyse the convexity of the generalized equivalent uniform dose equation (Niemierko A 1999 Med. Phys. 26 1100) when used either in the objective function or in the constraints. The practical importance of this analysis is that convex objective functions minimized over convex feasibility spaces do not have multiple local minima, likewise for concave objective functions maximized over convex feasibility spaces. Both of these situations are referred to as 'convex problems' and computationally efficient local search methods can be used for their solution. We also show that the Poisson-based tumour control probability objective function is strictly concave (if one neglects inter-patient heterogeneity), and hence it implies a single local minimum if maximized over a convex feasibility space. Even when including inter-patient heterogeneity, multiple local minima, although theoretically possible, are expected to be of minimal concern. The generalized equivalent uniform dose function (EUDa) is proved to be convex or concave depending on its only parameter a: when a is equal to or greater than 1, minimizing EUDa, on a convex feasibility space leads to a single minimum; when a is less than 1, maximizing EUDa, on a convex feasibility space leads to a single minimum. We also study a recently proposed practical, yet difficult, IMRT treatment planning formulation: unconstrained optimization of the objective function proposed by Wu et al (2002 Int. J. Radiat. Oncol. Biol. Phys. 52 224-35), which is expressed in terms of the EUDa for the target and normal tissues. This formulation may theoretically lead to multiple local minima. We propose a procedure for improving resulting solutions based on the convexity properties of the underlying objective function terms.

Algorithms↗
PubMed2002

Methodological issues in radiation dose-volume outcome analyses: summary of a joint AAPM/NIH workshop.

This report represents a summary of presentations at a joint workshop of the National Institutes of Health and the American Association of Physicists in Medicine (AAPM). Current methodological issues in dose-volume modeling are addressed here from several different perspectives. Areas of emphasis include (a) basic modeling issues including the equivalent uniform dose framework and the bootstrap method, (b) issues in the valid use of statistics, including the need for meta-analysis, (c) issues in dealing with organ deformation and its effects on treatment response, (d) evidence for volume effects for rectal complications, (e) the use of volume effect data in liver and lung as a basis for dose escalation studies, and (f) implications of uncertainties in volume effect knowledge on optimized treatment planning. Taken together, these approaches to studying volume effects describe many implications for the development and use of this information in radiation oncology practice. Areas of significant interest for further research include the meta-analysis of clinical data; interinstitutional pooled data analyses of volume effects; analyses of the uncertainties in outcome prediction models, minimal parameter number outcome models for ranking treatment plans (e.g., equivalent uniform dose); incorporation of the effect of motion in the outcome prediction; dose-escalation/isorisk protocols based on outcome models; the use of functional imaging to study radioresponse; and the need for further small animal tumor control probability/normal tissue complication probability studies.

Clinical Trials as Topic↗