Patient Specification Quality Assurance for Glioblastoma Multiforme Brain Tumors Treated with Intensity Modulated Radiation Therapy

The aim of this study was to evaluate the significance of performing patient specification quality assurance for patients diagnosed with glioblastoma multiforme treated with intensity modulated radiation therapy. The study evaluated ten intensity modulated radiation therapy treatment plans using 10 MV beams, a total dose of 60 Gy (2 Gy/fraction, five fractions a week for a total of six weeks treatment). For the quality assurance protocol we used a two-dimensional ionization-chamber array (2D-ARRAY). The results showed a very good agreement between the measured dose and the pretreatment planned dose. All the plans passed >95% gamma criterion with pixels within 5% dose difference and 3 mm distance to agreement. We concluded that using the 2D-ARRAY ion chamber for intensity modulated radiation therapy is an important step for intensity modulated radiation therapy treatment plans, and this study has shown that our treatment planning for intensity modulated radiation therapy is accurately done

Improved Approximation Algorithms for Segment Minimization in Intensity Modulated Radiation Therapy

he segment minimization problem consists of finding the smallest set of integer matrices that sum to a given intensity matrix, such that each summand has only one non-zero value, and the non-zeroes in each row are consecutive. This has direct applications in intensity-modulated radiation therapy, an effective form of cancer treatment. We develop three approximation algorithms for matrices with arbitrarily many rows. Our first two algorithms improve the approximation factor from the previous best of $1+\log_2 h$ to (roughly) $3/2 \cdot (1+\log_3 h)$ and $11/6\cdot(1+\log_4{h})$, respectively, where $h$ is the largest entry in the intensity matrix. We illustrate the limitations of the specific approach used to obtain these two algorithms by proving a lower bound of $\frac{(2b-2)}{b}\cdot\log_b{h} + \frac{1}{b}$ on the approximation guarantee. Our third algorithm improves the approximation factor from $2 \cdot (\log D+1)$ to $24/13 \cdot (\log D+1)$, where $D$ is (roughly) the largest difference between consecutive elements of a row of the intensity matrix. Finally, experimentation with these algorithms shows that they perform well with respect to the optimum and outperform other approximation algorithms on 77% of the 122 test cases we consider, which include both real world and synthetic data.Comment: 18 page

Intensity modulated radiation therapy for breast cancer: Current perspectives

open9noBackground: Owing to highly conformed dose distribution, intensity modulated radiation therapy (IMRT) has the potential to improve treatment results of radiotherapy (RT). Postoperative RT is a standard adjuvant treatment in conservative treatment of breast cancer (BC). The aim of this review is to analyze available evidence from randomized controlled trials (RCTs) on IMRT in BC, particularly in terms of reduction of side effects. Methods: A literature search of the bibliographic database PubMed, from January 1990 through November 2016, was performed. Only RCTs published in English were included. Results: Ten articles reporting data from 5 RCTs fulfilled the selection criteria and were included in our review. Three out of 5 studies enrolled only selected patients in terms of increased risk of toxicity. Three studies compared IMRT with standard tangential RT. One study compared the results of IMRT in the supine versus the prone position, and one study compared standard treatment with accelerated partial breast IMRT. Three studies reported reduced acute and/or late toxicity using IMRT compared with standard RT. No study reported improved quality of life. Conclusion: IMRT seems able to reduce toxicity in selected patients treated with postoperative RT for BC. Further analyses are needed to better define patients who are candidates for this treatment modality.openBuwenge, Milly; Cammelli, Silvia; Ammendolia, Ilario; Tolento, Giorgio; Zamagni, Alice; Arcelli, Alessandra; Macchia, Gabriella; Deodato, Francesco; Cilla, Savino; Morganti, Alessio G.Buwenge, Milly; Cammelli, Silvia; Ammendolia, Ilario; Tolento, Giorgio; Zamagni, Alice; Arcelli, Alessandra; Macchia, Gabriella; Deodato, Francesco; Cilla, Savino; Morganti, Alessio G

Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.Comment: 19 pages, 2 tables, and 5 figure

Intensity-modulated radiation therapy: emerging cancer treatment technology

The use of intensity-modulated radiation therapy (IMRT) is rapidly advancing in the field of radiation oncology. Intensity-modulated radiation therapy allows for improved dose conformality, thereby affording the potential to decrease the spectrum of normal tissue toxicities associated with IMRT. Preliminary results with IMRT are quite promising; however, the clinical data is relatively immature and overall patient numbers remain small. High-quality IMRT requires intensive physics support and detailed knowledge of three-dimensional anatomy and patterns of tumour spread. This review focuses on basic principles, and highlights the clinical implementation of IMRT in head and neck and prostate cancer

Treatment of pagetoid reticulosis with intensity modulated radiation therapy

Pagetoid reticulosis, also known as Woringer-Kolopp disease, is a rare variant of cutaneous T-cell lymphoma usually presenting as an isolated patch or plaque on the extremities.  Immunohistochemical staining of T-cell markers can be variable, but as the name implies the epidermotropic lymphocytes consistently display a “pagetoid” appearance.  The following case demonstrates clearance of this condition with intensity modulated radiation therapy, whereas most cases are managed with electron beam therapy if radiation therapy is implemented

Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning

Conventional planning objectives in optimization of intensity-modulated radiotherapy treatment (IMRT) plans are designed to minimize the violation of dose-volume histogram (DVH) thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more explicitly relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. We investigate the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives are generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume is observed in plans optimized within the proposed framework, indicating that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives

Accelerating two projection methods via perturbations with application to Intensity-Modulated Radiation Therapy

Constrained convex optimization problems arise naturally in many real-world applications. One strategy to solve them in an approximate way is to translate them into a sequence of convex feasibility problems via the recently developed level set scheme and then solve each feasibility problem using projection methods. However, if the problem is ill-conditioned, projection methods often show zigzagging behavior and therefore converge slowly. To address this issue, we exploit the bounded perturbation resilience of the projection methods and introduce two new perturbations which avoid zigzagging behavior. The first perturbation is in the spirit of $k$-step methods and uses gradient information from previous iterates. The second uses the approach of surrogate constraint methods combined with relaxed, averaged projections. We apply two different projection methods in the unperturbed version, as well as the two perturbed versions, to linear feasibility problems along with nonlinear optimization problems arising from intensity-modulated radiation therapy (IMRT) treatment planning. We demonstrate that for all the considered problems the perturbations can significantly accelerate the convergence of the projection methods and hence the overall procedure of the level set scheme. For the IMRT optimization problems the perturbed projection methods found an approximate solution up to 4 times faster than the unperturbed methods while at the same time achieving objective function values which were 0.5 to 5.1% lower.Comment: Accepted for publication in Applied Mathematics & Optimizatio

An Investigation Of Uncertainties In Intensity Modulated Radiation Therapy

Introduction: The treatment of cancer using intensity modulated radiation therapy (IMRT) is complex, involving many sub-processes to commission a treatment platform and treat patients. Uncertainties within the individual sub-process can lead to inaccurate treatments and sub-optimal patient outcomes. This research focused on minimizing uncertainties throughout IMRT commissioning and treatments. Methods: Five sub-processes were selected for uncertainty reduction: 1) optimizing radiation-imaging coincidence of the treatment machine, 2) improving the statistical model used to analyze IMRT commissioning data, 3) reducing the effect of ion recombination in IMRT quality assurance measurements, 4) improving the correlation between IMRT quality assurance results and patient-specific delivery inaccuracies, 5) commissioning of cranial stereotactic radiosurgery treatments. Results: Uncertainty reduction was achieved in all five sub-processes. Although the effect of this work on any single patient’s treatment is difficult to quantify, it is expected that the improvement in the sub-process accuracy will have a beneficial effect on the overall IMRT treatment delivery accuracy. Conclusion: Reducing uncertainties is an important aspect of radiotherapy quality assurance and should continue to be investigated for current and future treatment techniques