Incorporating variable RBE in IMPT optimization for ependymoma.

PURPOSE: To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity-modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)-based optimization. METHODS: This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard-of-care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose-averaged LET (LETd ) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LETd , LET-weighted dose, and equivalent uniform dose between the different optimization approaches. RESULTS: We found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans. CONCLUSION: We demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients.

Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem.

A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., m R 0) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method (m R 0= 0.002) outperformed the face-to-face diagnostic method (m R 0= 0.0153) by reducing m R 0 by 7.5 times.

Reflections on beam configuration optimization for intensity-modulated proton therapy.

Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT's therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.

A hybrid deep learning model for forecasting lymphocyte depletion during radiation therapy.

PURPOSE: Recent studies have shown that severe depletion of the absolute lymphocyte count (ALC) induced by radiation therapy (RT) has been associated with poor overall survival of patients with many solid tumors. In this paper, we aimed to predict radiation-induced lymphocyte depletion in esophageal cancer patients during the course of RT based on patient characteristics and dosimetric features. METHODS: We proposed a hybrid deep learning model in a stacked structure to predict a trend toward ALC depletion based on the clinical information before or at the early stages of RT treatment. The proposed model consisted of four channels, one channel based on long short-term memory (LSTM) network and three channels based on neural networks, to process four categories of features followed by a dense layer to integrate the outputs of four channels and predict the weekly ALC values. Moreover, a discriminative kernel was developed to extract temporal features and assign different weights to each part of the input sequence that enabled the model to focus on the most relevant parts. The proposed model was trained and tested on a dataset of 860 esophageal cancer patients who received concurrent chemoradiotherapy. RESULTS: The performance of the proposed model was evaluated based on several important prediction metrics and compared to other commonly used prediction models. The results showed that the proposed model outperformed off-the-shelf prediction methods with at least a 30% reduction in the mean squared error (MSE) of weekly ALC predictions based on pretreatment data. Moreover, using an extended model based on augmented first-week treatment, data reduced the MSE of predictions by 70% compared to the model based on the pretreatment data. CONCLUSIONS: In conclusion, our model performed well in predicting radiation-induced lymphocyte depletion for RT treatment planning. The ability to predict ALC will enable physicians to evaluate individual RT treatment plans for lymphopenia risk and to identify patients at high risk who would benefit from modified treatment approaches.

A reinforcement learning approach for finding optimal policy of adaptive radiation therapy considering uncertain tumor biological response.

Recent studies have shown that a tumor's biological response to radiation varies over time and has a dynamic nature. Dynamic biological features of tumor cells underscore the importance of using fractionation and adapting the treatment plan to tumor volume changes in radiation therapy treatment. Adaptive radiation therapy (ART) is an iterative process to adjust the dose of radiation in response to potential changes during the treatment. One of the key challenges in ART is how to determine the optimal timing of adaptations corresponding to tumor response to radiation. This paper aims to develop an automated treatment planning framework incorporating the biological uncertainties to find the optimal adaptation points to achieve a more effective treatment plan. First, a dynamic tumor-response model is proposed to predict weekly tumor volume regression during the period of radiation therapy treatment based on biological factors. Second, a Reinforcement Learning (RL) framework is developed to find the optimal adaptation points for ART considering the uncertainty in biological factors with the goal of achieving maximum final tumor control while minimizing or maintaining the toxicity level of the organs at risk (OARs) per the decision-maker's preference. Third, a beamlet intensity optimization model is solved using the predicted tumor volume at each adaptation point. The performance of the proposed RT treatment planning framework is tested using a clinical non-small cell lung cancer (NSCLC) case. The results are compared with the conventional fractionation schedule (i.e., equal dose fractionation) as a reference plan. The results show that the proposed approach performed well in achieving a robust optimal ART treatment plan under high uncertainty in the biological parameters. The ART plan outperformed the reference plan by increasing the mean biological effective dose (BED) value of the tumor by 2.01%, while maintaining the OAR BED within +0.5% and reducing the variability, in terms of the interquartile range (IQR) of tumor BED, by 25%.

Radiation-Induced Lymphopenia Risks of Photon Versus Proton Therapy for Esophageal Cancer Patients.

PURPOSE: To assess possible differences in radiation-induced lymphocyte depletion for esophageal cancer patients being treated with the following 3 treatment modalities: intensity-modulated radiation therapy (IMRT), passive scattering proton therapy (PSPT), and intensity-modulated proton therapy (IMPT). METHODS AND MATERIALS: We used 2 prediction models to estimate lymphocyte depletion based on dose distributions. Model I used a piecewise linear relationship between lymphocyte survival and voxel-by-voxel dose. Model II assumes that lymphocytes deplete exponentially as a function of total delivered dose. The models can be fitted using the weekly absolute lymphocyte counts measurements collected throughout treatment. We randomly selected 45 esophageal cancer patients treated with IMRT, PSPT, or IMPT at our institution (15 per modality) to demonstrate the fitness of the 2 models. A different group of 10 esophageal cancer patients who had received PSPT were included in this study of in silico simulations of multiple modalities. One IMRT and one IMPT plan were created, using our standards of practice for each modality, as competing plans to the existing PSPT plan for each patient. We fitted the models by PSPT plans used in treatment and predicted absolute lymphocyte counts for IMRT and IMPT plans. RESULTS: Model validation on each modality group of patients showed good agreement between measured and predicted absolute lymphocyte counts nadirs with mean squared errors from 0.003 to 0.023 among the modalities and models. In the simulation study of IMRT and IMPT on the 10 PSPT patients, the average predicted absolute lymphocyte count (ALC) nadirs were 0.27, 0.35, and 0.37 K/µL after IMRT, PSPT, and IMPT treatments using Model I, respectively, and 0.14, 0.22, and 0.33 K/µL using Model II. CONCLUSIONS: Proton plans carried a lower predicted risk of lymphopenia after the treatment course than did photon plans. Moreover, IMPT plans outperformed PSPT in terms of predicted lymphocyte preservation.

A biological effect-guided optimization approach using beam distal-edge avoidance for intensity-modulated proton therapy.

PURPOSE: Linear energy transfer (LET)-guided methods have been applied to intensity-modulated proton therapy (IMPT) to improve its biological effect. However, using LET as a surrogate for biological effect ignores the topological relationship of the scanning spot to different structures of interest. In this study, we developed an optimization method that takes advantage of the continuing increase in LET beyond the physical dose Bragg peak. This method avoids placing high biological effect values in critical structures and increases biological effect in the tumor area without compromising target coverage. METHODS: We selected the cases of two patients with brain tumors and two patients with head and neck tumors who had been treated with proton therapy at our institution. Three plans were created for each case: a plan based on conventional dose-based optimization (DoseOpt), one based on LET-incorporating optimization (LETOpt), and one based on the proposed distal-edge avoidance-guided optimization method (DEAOpt). In DEAOpt, an L1 -norm sparsity term, in which the penalty of each scanning spot was set according to the topological relationship between the organ positions and the location of the peak scaled LET-weighted dose (c LETxD) was added to a conventional dose-based optimization objective function. All plans were normalized to give the same target dose coverage. Dose (assuming a constant relative biological effectiveness value of 1.1, as in clinical practice), biological effect (c LETxD), and computing time consumption were evaluated and compared among the three optimization approaches for each patient case. RESULTS: For all four cases, all three optimization methods generated comparable dose coverage in both target and critical structures. The LETOpt plans and DEAOpt plans reduced biological effect hot spots in critical structures and increased biological effect in the target volumes to a similar extent. For the target, the c LETxD98% and c LETxD2% in the DEAOpt plans were on average 7.2% and 11.74% higher than in the DoseOpt plans, respectively. For the brainstem, the c LETxDmean in the DEAOpt plans was on average 33.38% lower than in the DoseOpt plans. In addition, the DEAOpt method saved 30.37% of the computation cost over the LETOpt method. CONCLUSIONS: DEAOpt is an alternative IMPT optimization approach that correlates the location of scanning spots with biological effect distribution. IMPT could benefit from the use of DEAOpt because this method not only delivers comparable biological effects to LETOpt plans, but also is faster.

Reply to Comment on 'Linear energy transfer incorporated intensity modulated proton therapy optimization'.

In this reply we present additional description of our linear model for LET optimization in response to the comments by Dr Gorissen. We clarify that this model cannot guarantee global optimal solutions to the sum-of-fractions problem. Based on our data, it could be used to optimize LET efficiently while dose constraints are maintained.

Design and Assessment Methodology for System Resilience Metrics.

By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.

Robust optimization to reduce the impact of biological effect variation from physical uncertainties in intensity-modulated proton therapy.

Robust optimization (RO) methods are applied to intensity-modulated proton therapy (IMPT) treatment plans to ensure their robustness in the face of treatment delivery uncertainties, such as proton range and patient setup errors. However, the impact of those uncertainties on the biological effect of protons has not been specifically considered. In this study, we added biological effect-based objectives into a conventional RO cost function for IMPT optimization to minimize the variation in biological effect. One brain tumor case, one prostate tumor case and one head & neck tumor case were selected for this study. Three plans were generated for each case using three different optimization approaches: planning target volume (PTV)-based optimization, conventional RO, and RO incorporating biological effect (BioRO). In BioRO, the variation in biological effect caused by IMPT delivery uncertainties was minimized for voxels in both target volumes and critical structures, in addition to a conventional voxel-based worst-case RO objective function. The biological effect was approximated by the product of dose-averaged linear energy transfer (LET) and physical dose. All plans were normalized to give the same target dose coverage, assuming a constant relative biological effectiveness (RBE) of 1.1. Dose, biological effect, and their uncertainties were evaluated and compared among the three optimization approaches for each patient case. Compared with PTV-based plans, RO plans achieved more robust target dose coverage and reduced biological effect hot spots in critical structures near the target. Moreover, with their sustained robust dose distributions, BioRO plans not only reduced variations in biological effect in target and normal tissues but also further reduced biological effect hot spots in critical structures compared with RO plans. Our findings indicate that IMPT could benefit from the use of conventional RO, which would reduce the biological effect in normal tissues and produce more robust dose distributions than those of PTV-based optimization. More importantly, this study provides a proof of concept that incorporating biological effect uncertainty gap into conventional RO would not only control the IMPT plan robustness in terms of physical dose and biological effect but also achieve further reduction of biological effect in normal tissues.

Linear energy transfer incorporated intensity modulated proton therapy optimization.

The purpose of this study was to investigate the feasibility of incorporating linear energy transfer (LET) into the optimization of intensity modulated proton therapy (IMPT) plans. Because increased LET correlates with increased biological effectiveness of protons, high LETs in target volumes and low LETs in critical structures and normal tissues are preferred in an IMPT plan. However, if not explicitly incorporated into the optimization criteria, different IMPT plans may yield similar physical dose distributions but greatly different LET, specifically dose-averaged LET, distributions. Conventionally, the IMPT optimization criteria (or cost function) only includes dose-based objectives in which the relative biological effectiveness (RBE) is assumed to have a constant value of 1.1. In this study, we added LET-based objectives for maximizing LET in target volumes and minimizing LET in critical structures and normal tissues. Due to the fractional programming nature of the resulting model, we used a variable reformulation approach so that the optimization process is computationally equivalent to conventional IMPT optimization. In this study, five brain tumor patients who had been treated with proton therapy at our institution were selected. Two plans were created for each patient based on the proposed LET-incorporated optimization (LETOpt) and the conventional dose-based optimization (DoseOpt). The optimized plans were compared in terms of both dose (assuming a constant RBE of 1.1 as adopted in clinical practice) and LET. Both optimization approaches were able to generate comparable dose distributions. The LET-incorporated optimization achieved not only pronounced reduction of LET values in critical organs, such as brainstem and optic chiasm, but also increased LET in target volumes, compared to the conventional dose-based optimization. However, on occasion, there was a need to tradeoff the acceptability of dose and LET distributions. Our conclusion is that the inclusion of LET-dependent criteria in the IMPT optimization could lead to similar dose distributions as the conventional optimization but superior LET distributions in target volumes and normal tissues. This may have substantial advantages in improving tumor control and reducing normal tissue toxicities.

Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet tight dose limits. For robust optimization, the worst case dose approach was less sensitive to uncertainties than was the minmax approach for the prostate and skull-based cancer patients, whereas the minmax approach was superior for the head and neck cancer patients. The robustness of the IMPT plans was remarkably better after robust optimization than after PTV-based optimization, and the NLP-PTV-based optimization outperformed the LP-PTV-based optimization regarding robustness of clinical target volume coverage. In addition, plans generated using LP-based methods had notably fewer scanning spots than did those generated using NLP-based methods.

Robust Optimization for Intensity Modulated Proton Therapy Plans with Multi-Isocenter Large Fields.

We have developed a robust optimization approach for intensity modulated proton therapy treatment plans with multi-isocenter large fields. The method creates a low-gradient field dose in the junction regions to mitigate the impact caused by misalignment errors and is more efficient than the conventional junction shifting technique.

Improved beam angle arrangement in intensity modulated proton therapy treatment planning for localized prostate cancer.

PURPOSE: This study investigates potential gains of an improved beam angle arrangement compared to a conventional fixed gantry setup in intensity modulated proton therapy (IMPT) treatment for localized prostate cancer patients based on a proof of principle study. MATERIALS AND METHODS: Three patients with localized prostate cancer retrospectively selected from our institution were studied. For each patient, IMPT plans were designed using two, three and four beam angles, respectively, obtained from a beam angle optimization algorithm. Those plans were then compared with ones using two lateral parallel-opposed beams according to the conventional planning protocol for localized prostate cancer adopted at our institution. RESULTS: IMPT plans with two optimized angles achieved significant improvements in rectum sparing and moderate improvements in bladder sparing against those with two lateral angles. Plans with three optimized angles further improved rectum sparing significantly over those two-angle plans, whereas four-angle plans found no advantage over three-angle plans. A possible three-beam class solution for localized prostate patients was suggested and demonstrated with preserved dosimetric benefits because individually optimized three-angle solutions were found sharing a very similar pattern. CONCLUSIONS: This study has demonstrated the potential of using an improved beam angle arrangement to better exploit the theoretical dosimetric benefits of proton therapy and provided insights of selecting quality beam angles for localized prostate cancer treatment.

A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints.

OBJECTIVES: Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems. METHOD: A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China. RESULTS: The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in individual maximum overtime (OT); and (4) an 42% reduction in the total OT of nurses. Comparison results with the real 10-workday hospital scheduling further show the advantage of the ACO in several measurements. Instead of assigning all surgeries by a surgeon to only one OR and the same nurses by traditional manual approach in hospital, ACO realizes a more balanced surgery arrangement by assigning the surgeries to different ORs and nurses. It eventually leads to shortening the end time within the confidential interval of [7.4%, 24.6%] with 95% confidence level. CONCLUSION: The ACO approach proposed in this paper efficiently solves the surgery scheduling problem with daily nurse roster while providing a shortened end time and relatively balanced resource allocations. It also supports the advantage of integrating the surgery scheduling with the nurse scheduling and the efficiency of systematic optimization considering a complete three-stage surgery flow and resources involved.

An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT.

Prescriptions for radiation therapy are given in terms of dose-volume constraints (DVCs). Solving the fluence map optimization (FMO) problem while satisfying DVCs often requires a tedious trial-and-error for selecting appropriate dose control parameters on various organs. In this paper, we propose an iterative approach to satisfy DVCs using a multi-objective linear programming (LP) model for solving beamlet intensities. This algorithm, starting from arbitrary initial parameter values, gradually updates the values through an iterative solution process toward optimal solution. This method finds appropriate parameter values through the trade-off between OAR sparing and target coverage to improve the solution. We compared the plan quality and the satisfaction of the DVCs by the proposed algorithm with two nonlinear approaches: a nonlinear FMO model solved by using the L-BFGS algorithm and another approach solved by a commercial treatment planning system (Eclipse 8.9). We retrospectively selected from our institutional database five patients with lung cancer and one patient with prostate cancer for this study. Numerical results show that our approach successfully improved target coverage to meet the DVCs, while trying to keep corresponding OAR DVCs satisfied. The LBFGS algorithm for solving the nonlinear FMO model successfully satisfied the DVCs in three out of five test cases. However, there is no recourse in the nonlinear FMO model for correcting unsatisfied DVCs other than manually changing some parameter values through trial and error to derive a solution that more closely meets the DVC requirements. The LP-based heuristic algorithm outperformed the current treatment planning system in terms of DVC satisfaction. A major strength of the LP-based heuristic approach is that it is not sensitive to the starting condition.

Evaluation and mitigation of the interplay effects of intensity modulated proton therapy for lung cancer in a clinical setting.

PURPOSE: The primary aim of this study was to evaluate the impact of the interplay effects of intensity modulated proton therapy (IMPT) plans for lung cancer in the clinical setting. The secondary aim was to explore the technique of isolayered rescanning to mitigate these interplay effects. METHODS AND MATERIALS: A single-fraction 4-dimensional (4D) dynamic dose without considering rescanning (1FX dynamic dose) was used as a metric to determine the magnitude of dosimetric degradation caused by 4D interplay effects. The 1FX dynamic dose was calculated by simulating the machine delivery processes of proton spot scanning on a moving patient, described by 4D computed tomography during IMPT delivery. The dose contributed from an individual spot was fully calculated on the respiratory phase that corresponded to the life span of that spot, and the final dose was accumulated to a reference computed tomography phase by use of deformable image registration. The 1FX dynamic dose was compared with the 4D composite dose. Seven patients with various tumor volumes and motions were selected for study. RESULTS: The clinical target volume (CTV) prescription coverage for the 7 patients was 95.04%, 95.38%, 95.39%, 95.24%, 95.65%, 95.90%, and 95.53% when calculated with the 4D composite dose and 89.30%, 94.70%, 85.47%, 94.09%, 79.69%, 91.20%, and 94.19% when calculated with the 1FX dynamic dose. For these 7 patients, the CTV coverage calculated by use of a single-fraction dynamic dose was 95.52%, 95.32%, 96.36%, 95.28%, 94.32%, 95.53%, and 95.78%, with a maximum monitor unit limit value of 0.005. In other words, by increasing the number of delivered spots in each fraction, the degradation of CTV coverage improved up to 14.6%. CONCLUSIONS: A single-fraction 4D dynamic dose without rescanning was validated as a surrogate to evaluate the interplay effects of IMPT for lung cancer in the clinical setting. The interplay effects potentially can be mitigated by increasing the amount of isolayered rescanning in each fraction delivery.

Proton energy optimization and reduction for intensity-modulated proton therapy.

Intensity-modulated proton therapy (IMPT) is commonly delivered via the spot-scanning technique. To 'scan' the target volume, the proton beam is controlled by varying its energy to penetrate the patient's body at different depths. Although scanning the proton beamlets or spots with the same energy can be as fast as 10-20 m s(-1), changing from one proton energy to another requires approximately two additional seconds. The total IMPT delivery time thus depends mainly on the number of proton energies used in a treatment. Current treatment planning systems typically use all proton energies that are required for the proton beam to penetrate in a range from the distal edge to the proximal edge of the target. The optimal selection of proton energies has not been well studied. In this study, we sought to determine the feasibility of optimizing and reducing the number of proton energies in IMPT planning. We proposed an iterative mixed-integer programming optimization method to select a subset of all available proton energies while satisfying dosimetric criteria. We applied our proposed method to six patient datasets: four cases of prostate cancer, one case of lung cancer, and one case of mesothelioma. The numbers of energies were reduced by 14.3%-18.9% for the prostate cancer cases, 11.0% for the lung cancer cases and 26.5% for the mesothelioma case. The results indicate that the number of proton energies used in conventionally designed IMPT plans can be reduced without degrading dosimetric performance. The IMPT delivery efficiency could be improved by energy layer optimization leading to increased throughput for a busy proton center in which a delivery system with slow energy switch is employed.

On the interplay effects with proton scanning beams in stage III lung cancer.

PURPOSE: To assess the dosimetric impact of interplay between spot-scanning proton beam and respiratory motion in intensity-modulated proton therapy (IMPT) for stage III lung cancer. METHODS: Eleven patients were sampled from 112 patients with stage III nonsmall cell lung cancer to well represent the distribution of 112 patients in terms of target size and motion. Clinical target volumes (CTVs) and planning target volumes (PTVs) were defined according to the authors' clinical protocol. Uniform and realistic breathing patterns were considered along with regular- and hypofractionation scenarios. The dose contributed by a spot was fully calculated on the computed tomography (CT) images corresponding to the respiratory phase that the spot is delivered, and then accumulated to the reference phase of the 4DCT to generate the dynamic dose that provides an estimation of what might be delivered under the influence of interplay effect. The dynamic dose distributions at different numbers of fractions were compared with the corresponding 4D composite dose which is the equally weighted average of the doses, respectively, computed on respiratory phases of a 4DCT image set. RESULTS: Under regular fractionation, the average and maximum differences in CTV coverage between the 4D composite and dynamic doses after delivery of all 35 fractions were no more than 0.2% and 0.9%, respectively. The maximum differences between the two dose distributions for the maximum dose to the spinal cord, heart V40, esophagus V55, and lung V20 were 1.2 Gy, 0.1%, 0.8%, and 0.4%, respectively. Although relatively large differences in single fraction, correlated with small CTVs relative to motions, were observed, the authors' biological response calculations suggested that this interfractional dose variation may have limited biological impact. Assuming a hypofractionation scenario, the differences between the 4D composite and dynamic doses were well confined even for single fraction. CONCLUSIONS: Despite the presence of interplay effect, the delivered dose may be reliably estimated using the 4D composite dose. In general the interplay effect may not be a primary concern with IMPT for lung cancers for the authors' institution. The described interplay analysis tool may be used to provide additional confidence in treatment delivery.

Incorporating deliverable monitor unit constraints into spot intensity optimization in intensity-modulated proton therapy treatment planning.

The purpose of this study is to investigate the feasibility and impact of incorporating deliverable monitor unit (MU) constraints into spot intensity optimization (SIO) in intensity-modulated proton therapy (IMPT) treatment planning. The current treatment planning system (TPS) for IMPT disregards deliverable MU constraints in the SIO routine. It performs a post-processing procedure on an optimized plan to enforce deliverable MU values that are required by the spot scanning proton delivery system. This procedure can create a significant dose distribution deviation between the optimized and post-processed deliverable plans, especially when small spot spacings are used. In this study, we introduce a two-stage linear programming approach to optimize spot intensities and constrain deliverable MU values simultaneously, i.e., a deliverable SIO (DSIO) model. Thus, the post-processing procedure is eliminated and the associated optimized plan deterioration can be avoided. Four prostate cancer cases at our institution were selected for study and two parallel opposed beam angles were planned for all cases. A quadratic programming based model without MU constraints, i.e., a conventional SIO (CSIO) model, was also implemented to emulate commercial TPS. Plans optimized by both the DSIO and CSIO models were evaluated for five different settings of spot spacing from 3 to 7 mm. For all spot spacings, the DSIO-optimized plans yielded better uniformity for the target dose coverage and critical structure sparing than did the CSIO-optimized plans. With reduced spot spacings, more significant improvements in target dose uniformity and critical structure sparing were observed in the DSIO than in the CSIO-optimized plans. Additionally, better sparing of the rectum and bladder was achieved when reduced spacings were used for the DSIO-optimized plans. The proposed DSIO approach ensures the deliverability of optimized IMPT plans that take into account MU constraints. This eliminates the post-processing procedure required by the TPS as well as the resultant deteriorating effect on ultimate dose distributions. This approach therefore allows IMPT plans to adopt all possible spot spacings optimally. Moreover, dosimetric benefits can be achieved using smaller spot spacings.