The use of a multiobjective evolutionary algorithm to increase flexibility in the search for better IMRT plans.

PURPOSE: To evaluate how a more flexible and thorough multiobjective search of feasible IMRT plans affects performance in IMRT optimization. METHODS: A multiobjective evolutionary algorithm (MOEA) was used as a tool to investigate how expanding the search space to include a wider range of penalty functions affects the quality of the set of IMRT plans produced. The MOEA uses a population of IMRT plans to generate new IMRT plans through deterministic minimization of recombined penalty functions that are weighted sums of multiple, tissue-specific objective functions. The quality of the generated plans are judged by an independent set of nonconvex, clinically relevant decision criteria, and all dominated plans are eliminated. As this process repeats itself, better plans are produced so that the population of IMRT plans will approach the Pareto front. Three different approaches were used to explore the effects of expanding the search space. First, the evolutionary algorithm used genetic optimization principles to search by simultaneously optimizing both the weights and tissue-specific dose parameters in penalty functions. Second, penalty function parameters were individually optimized for each voxel in all organs at risk (OARs) in the MOEA. Finally, a heuristic voxel-specific improvement (VSI) algorithm that can be used on any IMRT plan was developed that incrementally improves voxel-specific penalty function parameters for all structures (OARs and targets). Different approaches were compared using the concept of domination comparison applied to the sets of plans obtained by multiobjective optimization. RESULTS: MOEA optimizations that simultaneously searched both importance weights and dose parameters generated sets of IMRT plans that were superior to sets of plans produced when either type of parameter was fixed for four example prostate plans. The amount of improvement increased with greater overlap between OARs and targets. Allowing the MOEA to search for voxel-specific penalty functions improved results for simple cases with three structures but did not improve results for a more complex case with seven structures. For this modification, the amount of improvement increased with less overlap between OARs and targets. The voxel-specific improvement algorithm improved results for all cases, and its clinical relevance was demonstrated in a complex prostate and a very complex head and neck case. CONCLUSIONS: Using an evolutionary algorithm as a tool, it was found that allowing more flexibility in the search space enhanced performance. The two strategies of (a) varying the weights and reference doses in the objective function and (b) removing the constraint of equal penalties for all voxels in a structure both generated sets of plans that dominated sets of plans considered to be "Pareto optimal" within the conventional, more limited search space. When considering voxel-specific objectives, the very large search space can lead to convergence problems in the MOEA for complex cases, but this is not an issue for the VSI algorithm.

When is better best? A multiobjective perspective.

PURPOSE: To identify the most informative methods for reporting results of treatment planning comparisons. METHODS: Seven articles from the past year of International Journal of Radiation Oncology Biology Physics reported on comparisons of treatment plans for IMRT and IMAT. The articles were reviewed to identify methods of comparisons. Decision theoretical concepts were used to evaluate the study methods and highlight those that provide the most information. RESULTS: None of the studies examined the correlation between objectives. Statistical comparisons provided some information but not enough to provide support for a robust decision analysis. CONCLUSIONS: The increased use of treatment planning studies to evaluate different methods in radiation therapy requires improved standards for designing the studies and reporting the results.

Investigation of effective decision criteria for multiobjective optimization in IMRT.

PURPOSE: To investigate how using different sets of decision criteria impacts the quality of intensity modulated radiation therapy (IMRT) plans obtained by multiobjective optimization. METHODS: A multiobjective optimization evolutionary algorithm (MOEA) was used to produce sets of IMRT plans. The MOEA consisted of two interacting algorithms: (i) a deterministic inverse planning optimization of beamlet intensities that minimizes a weighted sum of quadratic penalty objectives to generate IMRT plans and (ii) an evolutionary algorithm that selects the superior IMRT plans using decision criteria and uses those plans to determine the new weights and penalty objectives of each new plan. Plans resulting from the deterministic algorithm were evaluated by the evolutionary algorithm using a set of decision criteria for both targets and organs at risk (OARs). Decision criteria used included variation in the target dose distribution, mean dose, maximum dose, generalized equivalent uniform dose (gEUD), an equivalent uniform dose (EUD(alpha,beta) formula derived from the linear-quadratic survival model, and points on dose volume histograms (DVHs). In order to quantatively compare results from trials using different decision criteria, a neutral set of comparison metrics was used. For each set of decision criteria investigated, IMRT plans were calculated for four different cases: two simple prostate cases, one complex prostate Case, and one complex head and neck Case. RESULTS: When smaller numbers of decision criteria, more descriptive decision criteria, or less anti-correlated decision criteria were used to characterize plan quality during multiobjective optimization, dose to OARs and target dose variation were reduced in the final population of plans. Mean OAR dose and gEUD (a = 4) decision criteria were comparable. Using maximum dose decision criteria for OARs near targets resulted in inferior populations that focused solely on low target variance at the expense of high OAR dose. Target dose range, (D(max) - D(min)), decision criteria were found to be most effective for keeping targets uniform. Using target gEUD decision criteria resulted in much lower OAR doses but much higher target dose variation. EUD(alpha,beta) based decision criteria focused on a region of plan space that was a compromise between target and OAR objectives. None of these target decision criteria dominated plans using other criteria, but only focused on approaching a different area of the Pareto front. CONCLUSIONS: The choice of decision criteria implemented in the MOEA had a significant impact on the region explored and the rate of convergence toward the Pareto front. When more decision criteria, anticorrelated decision criteria, or decision criteria with insufficient information were implemented, inferior populations are resulted. When more informative decision criteria were used, such as gEUD, EUD(alpha,beta), target dose range, and mean dose, MOEA optimizations focused on approaching different regions of the Pareto front, but did not dominate each other. Using simple OAR decision criteria and target EUD(alpha,beta) decision criteria demonstrated the potential to generate IMRT plans that significantly reduce dose to OARs while achieving the same or better tumor control when clinical requirements on target dose variance can be met or relaxed.

A hierarchical evolutionary algorithm for multiobjective optimization in IMRT.

PURPOSE: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. METHODS: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. RESULTS: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12-15 plans, any random plan selected from a MOEA population had a 11.3% +/- 0.7% chance of dominating any random plan selected by a standard genetic package with 0.04% +/- 0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations. CONCLUSIONS: The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation.

Outcomes following radiation for cutaneous squamous cell carcinoma of the head and neck: Associations between immune suppression and recurrence.

BACKGROUND: Data evaluating outcomes and patterns of recurrence following radiation therapy (RT) for cutaneous squamous cell carcinoma (cSCC) of the head and neck are limited. METHODS: We performed a retrospective analysis of 111 head and neck cSCC patients treated with RT at 4 affiliated institutions. RESULTS: With median follow-up of 7 months, there were 29 (26%) recurrences, 73% of which were nodal (n = 21). Immunosuppression (IS) was the only factor associated with recurrence (47% in IS, 22% in non-IS, P = .04), and also with time to recurrence in multivariate analysis (HR 5.5; P = .03). No factors were associated with recurrence among patients who received definitive RT. The majority of patients who recurred were salvaged with surgery (n = 20, 69%). CONCLUSION: In a cohort of cSCC treated with radiotherapy, there was an association between IS and increased failure risk. The majority of failures were salvaged surgically.

Impact of acquisition geometry, image processing, and patient size on lesion detection in whole-body 18F-FDG PET.

UNLABELLED: The aim of this work was to develop a rigorous evaluation methodology to assess performance of different acquisition and processing methods for variable patient sizes in the context of lesion detection in whole-body (18)F-FDG PET. METHODS: Fifty-nine bed positions were acquired in 32 patients in 2-dimensional (2D) and 3-dimensional (3D) modes 1-4 h after (18)F-FDG injection (740 MBq) using a BGO PET scanner. Three spheres (1.0-, 1.3-, and 1.6-cm diameter) containing (68)Ge were also imaged separately in air, at locations corresponding to possible lesion sites in 2D and 3D (590 targets per condition). Each bed position was acquired for 7 min in 2D and 6 min in 3D and corrected for randoms using delayed window randoms subtraction (DWS) or randoms variance reduction (RVR). Sphere sinograms were attenuated using the 2D or 3D attenuation map derived from the transmission scan of the patient, after scaling 2D and 3D sinograms with identical factors to ensure marginal detectability. Resulting 2D sinograms were reconstructed with filtered backprojection (FBP) and ordered-subsets expectation maximization (OSEM) without any scatter or attenuation correction (FBP-NATS and OSEM-NATS) or corrected for scatter and attenuation and reconstructed using FBP (FBP-ATT) or attenuation-weighted OSEM (AWOSEM). 3D sinograms were processed identically after Fourier rebinning. Next, reconstructed volumes were compared on the basis of performance of a 3-channel Hotelling observer (CHO-SNR [SNR is signal-to-noise ratio]) in detecting the presence of a sphere of unknown size on an anatomic background while modeling observer noise. The noise equivalent count (NEC) rate was computed in 2D and 3D for 3 different phantoms sizes (40, 60, and 95 kg) and compared with lesion detection SNR. RESULTS: 3D imaging yielded better lesion detectability than 2D (P < 0.025, 2-tailed paired t test) in patients of normal size (body mass index [BMI] < or = 31). However, 2D imaging yielded better lesion detectability than 3D in large patients (BMI > 31), as 3D performance deteriorated in large patients (P < 0.05). 2D and 3D yielded similar results for different lesion sizes. CHO-SNR were 40% greater for AWOSEM, FBP-ATT, and FBPNAT than for OSEM (P < 0.05), and AWOSEM yielded significantly better lesion detectability than did FBP. In all patients, RVR yielded a systematic improvement in CHO-SNR over DWS in both 2D and 3D. radicalNEC was characterized by a behavior similar to that of SNR(CHO) for the 3 different phantom sizes considered in this study.

CT and PET: early prognostic indicators of response to imatinib mesylate in patients with gastrointestinal stromal tumor.

OBJECTIVE: We report results from a pilot study aimed at optimizing the use of CT bidimensional measurements and 18F-FDG PET maximum standardized uptake values (SUVs-(max)) for determining response to prolonged imatinib mesylate treatment in patients with advanced gastrointestinal stromal tumors (GISTs). SUBJECTS AND METHODS: Sixty-three patients enrolled in a multicenter trial evaluating imatinib mesylate therapy for advanced GIST underwent FDG PET at baseline and 1 month after initiation of treatment. Of these 63 patients, 58 underwent concomitant CT. Time-to-treatment failure (TTF) was used as the outcome measure. Patients were followed up over a range of 23.7 to 37 months (median, 31.7 months). The predictive power of change in CT bidimensional measurements, change in PET SUVmax, and PET SUVmax at 1 month after initiation of treatment were determined, optimized, and compared. The effectiveness of combining metrics was also evaluated. RESULTS: Both a threshold PET SUVmax value of 2.5 at 1 month (p = 0.04) and the European Organization for Research and Treatment of Cancer (EORTC) criteria for partial response on FDG PET (25% reduction in PET SUVmax) at 1 month (p = 0.004) were predictive of prolonged treatment success. The Southwest Oncology Group (SWOG) criteria for partial response ((3) 50% reduction in CT bidimensional measurements) at 1 month were not predictive (p = 0.55) of TTF. Optimizing metrics improved results performance. An optimized PET SUVmax threshold of 3.4 (p = 0.00002), a reduction in the SUVmax of 40% (p = 0.002), and an optimized CT bidimensional measurement threshold--that is, no growth from baseline to 1 month (p = 0.00005)--outperformed the existing standards (i.e., EORTC and SWOG criteria). Combinations of metrics did not improve performance. CONCLUSION: The two best metrics were the optimized PET SUVmax threshold of 3.4 at 1 month (p = 0.00002) and the optimized CT bidimensional measurement threshold (no growth from baseline to 1 month, p = 0.00005) in this patient group.

Computerized System for Safety Verification of External Beam Radiation Therapy Planning.

PURPOSE: To report an assessment of in-house software, Verifier, developed to improve efficacy and efficiency of the radiation therapy (RT) treatment planning process and quality control review (QCR). METHODS AND MATERIALS: Radiation therapy plan parameters retrieved from our treatment planning database are used by automated tests to give 75 types of warnings, such as prescription and plan discrepancies. The software is continuously updated on the basis of new issues, ideas, and planning policies. Verifier was retrospectively assessed (2007-2015) by examining impact on treatment plan revisions, frequency of quality improvement incident reports of avoidable RT plan-related safety events, unaddressed issues, and staff efficiency. RESULTS: Plan revisions for specific issues declined dramatically in response to implementation of corresponding Verifier tests. Between 2012 and 2015 our institution's total rate of plan revisions dropped from 18.0% to 11.2%. Between 2008 and 2015 specific tests were added to Verifier while the rate of corresponding avoidable safety events was reduced from 0.34% to 0.00% over the same period. Simulations suggest Verifier saves approximately 2 to 5 minutes per QCR. CONCLUSIONS: The decrease in quantifiable metrics of plan revisions and incident reports suggests automatic RT plan-checking software enhances patient safety and clinical efficiency. Although only modest time savings may be gained using Verifier for the QCR itself, the greater impact on efficiency is through avoiding late-stage plan modifications and improving documentation via automation. We encourage other institutions to consider working toward adding similar technologies to enhance their RT quality assurance programs.

Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.

PURPOSE: To build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making. METHODS: An in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans. RESULTS: The Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study. CONCLUSIONS: The radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.

Toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma.

PURPOSE: To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol. METHODS AND MATERIALS: We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients. Patient-individualized, spherically-symmetric simulations of the standard-of-care and optimized plans were compared in terms of several biological metrics. RESULTS: The integrated model generated spatially non-uniform doses that, when compared to the standard-of-care protocol, resulted in a 67% to 93% decrease in equivalent uniform dose to normal tissue, while the therapeutic ratio, the ratio of tumor equivalent uniform dose to that of normal tissue, increased between 50% to 265%. Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized plans would have a significant impact on delaying tumor progression, with increases from 21% to 105% for 9 of 11 patients. CONCLUSIONS: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for radiation therapy generated biologically-guided doses that decreased normal tissue EUD and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma.

Scripting in radiation therapy: an automatic 3D beam-naming system.

Scripts can be executed within the radiation treatment planning software framework to reduce human error, increase treatment planning efficiency, reduce confusion, and promote consistency within an institution or even among institutions. Scripting is versatile, and one application is an automatic 3D beam-naming system that describes the position of the beam relative to the patient in 3D space. The naming system meets the need for nomenclature that is conducive for clear and accurate communication of beam entry relative to patient anatomy. In radiation oncology in particular, where miscommunication can cause significant harm to patients, a system that minimizes error is essential. Frequent sharing of radiation treatment information occurs not only among members within a department but also between different treatment centers. Descriptions of treatment beams are perhaps the most commonly shared information about a patient's course of treatment in radiation oncology. Automating the naming system by the use of a script reduces the potential for human error, improves efficiency, enforces consistency, and would allow an institution to convert to a new naming system with greater ease. This script has been implemented in the Department of Radiation Oncology at the University of Washington Medical Center since December 2009. It is currently part of the dosimetry protocol and is accessible by medical dosimetrists, radiation oncologists, and medical physicists. This paper highlights the advantages of using an automatic 3D beam-naming script to flawlessly and quickly identify treatment beams with unique names. Scripting in radiation treatment planning software has many uses and great potential for improving clinical care.