Citations

Abstract Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams – a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.

Abstract Lung cancer remains the leading cause of cancer-related death, partly due to the fact that most patients are diagnosed with metastatic disease, which is the main cause of death, yet effective therapeutics are lacking. Utilizing mouse models of metastatic lung adenocarcinomas, we report that the transcription factor GATA3 and its cofactor FOG2 play critical roles in lung adenocarcinoma metastasis by promoting the expression of the lysyl hydroxylase (LH) family members, which in turn regulate the tumorigenicity and motility of lung tumor cells through both EMT and EMT-independent mechanisms. In vivo, LHs are elevated in human lung adenocarcinomas and are associated with worse patient survival. Collectively, our data suggest that targeting LHs may be useful for treating metastatic lung cancer. Citation Format: Wei Liu, Ting Zhang, Lixia Guo, Yuanyuan Wang, Yanan Yang. Role of lysyl hydroxylases in metastatic lung cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr B07.

PurposeThe purpose of this study was to develop a fast method for proton range quality assurance (QA) using a dual step wedge and two‐dimensional (2D) scintillator and to evaluate the robustness, sensitivity, and long‐term reproducibility of this method.MethodsAn in‐house customized dual step wedge and a 2D scintillator were developed to measure proton ranges. Proton beams with homogenous fluence were delivered through wedge, and the images captured by the scintillator were used to calculate the proton ranges by a simple trigonometric method. The range measurements of 97 energies, comprising all clinically available synchrotron energies at our facility (ranges varying from 4 to 32 cm) were repeated ten times in all four gantry rooms for range baseline values. They were then used for evaluating room‐to‐room range consistencies. The robustness to setup uncertainty was evaluated by measuring ranges with ±2 mm setup deviations in the x, y, and z directions. The long‐term reproducibility was evaluated by 1 month of daily range measurements by this method.ResultsRanges of all 97 energies were measured in less than 10 minutes including setup time. The reproducibility in a single day and daily over 1 month is within 0.1 and 0.15 mm, respectively. The method was very robust to setup uncertainty, with measured range consistencies within 0.15 mm for ±2 mm couch shifts. The method was also sensitive enough for validating range consistencies among gantry rooms and for detecting small range variations.ConclusionsThe new method of using a dual step wedge and scintillator for proton range QA was efficient, highly reproducible, and robust. This method of proton range QA was highly feasible and appealing from a workflow point of view.

PurposeThe aim of this work was to develop an efficient daily quality assurance (QA) program with strict tolerance levels for pencil beam scanning (PBS) proton radiotherapy featuring simultaneous dosimetric testing on a single, nonuniform field.MethodsA nonuniform field measuring beam output, proton range, and spot position was designed for delivery onto a Sun Nuclear Daily‐QA 3 device. A custom acrylic block permitted simultaneous measurement of low‐ and high‐energy proton ranges in addition to beam output. Sensitivities to output, range, and spot position were evaluated to quantitate the device's response. Reproducibility tests were used to identify and control sources of measurement error as well as to assess the QA procedure's robustness. This procedure was implemented in each of our four treatment rooms independently; 4–6 months of daily QA measurements were collected.ResultsThe 1% output, 0.5 mm range, and 1.5 mm spot position tolerances derived from preliminary tests were tighter overall than tolerances found in the literature and equivalent to the limits used for proton system commissioning. The simplicity and automation of the procedure reduced the time required for daily QA to 10 min per treatment room, and competition for beam between multiple treatment rooms was minimized.ConclusionsAn efficient daily PBS QA procedure can be performed using a single, nonuniform field on a nondedicated QA device. A thorough quantitation of the device's response and careful control of measurement uncertainties allowed daily tolerances to match commissioning standards.

PurposeCurrently, in clinical practice of intensity‐modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization.Methods and materialsCao et al. have demonstrated a two‐stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings.ResultThe new method achieved better plan quality than the conventional method. The D95% of the clinical target volume (CTV) normalized to the prescription dose was (mean [min–max]): (99.4% [99.2%–99.6%]) vs. (99.2% [98.6%–99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose–volume histogram band gap (narrower band gap means more robust plans) at D95% normalized to the prescription dose was (mean [min–max]): (1.5% [0.5%–4.3%]) vs. (1.2% [0.6%–3.8%]).ConclusionOur new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine‐deliverable plans with better tumor coverage while maintaining high‐plan robustness.

Purpose We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity‐modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs. Methods We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The “worst‐case robust optimization” model was applied. One additional term as a “biological surrogate (BS)” of OARs due to the high LET‐related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose‐averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in‐house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst‐case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited‐memory Broyden–Fletcher–Goldfarb–Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose–volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods. Results For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model. Conclusions Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.

AbstractMonte Carlo (MC) simulation has been used to generate commissioning data for the beam modeling of treatment planning system (TPS). We have developed a method called radial projection (RP) for postprocessing of MC‐simulation‐generated data. We used the RP method to reduce the statistical uncertainty of the lateral profile of proton pencil beams with axial symmetry. The RP method takes advantage of the axial symmetry of dose distribution to use the mean value of multiple independent scores as the representative score. Using the mean as the representative value rather than any individual score results in substantial reduction in statistical uncertainty. Herein, we present the concept and step‐by‐step implementation of the RP method, as well as show the advantage of the RP method over conventional measurement methods for generating lateral profile. Lateral profiles generated by both methods were compared to demonstrate the uncertainty reduction qualitatively, and standard error comparison was performed to demonstrate the reduction quantitatively. The comparisons showed that statistical uncertainty was reduced substantially by the RP method. Using the RP method to postprocess MC data, the corresponding MC simulation time was reduced by a factor of 10 without quality reduction in the generated result from the MC data. We concluded that the RP method is an effective technique to increase MC simulation efficiency for generating lateral profiles for axially symmetric pencil beams.