Purpose: Emergency response and medical preparedness for radiological incidents is one of the critical cornerstones for Homeland Security, along with biological and chemical incidents. The recent Fukushima Daiichi nuclear plant incidents underscore the paramount importance of such preparedness and response capability. Such needs are wide‐spread as many nations employ nuclear plants for energy generation. In this work, we focus on development and deployment of a real‐time simulation and decision support system, RealOpt‐CRC, along with the knowledge data bank that can be used by regional/local radiation/public health administrators to prepare for and deal with radiological emergency situations. Methods: Large‐scale simulator for modeling systems operations and performance, computer graphics for mouse‐click facility design and optimization for resource allocation are designed and implemented into a web‐base secured system. The RealOpt system offers operations capability to i)rapidly setup shelters to house the displaced/at‐risk population, ii)determine optimal resource allocation and operations for rapid screening and decontamination; iii)recommend and facilitate practical steps to minimize exposure risk; iv)perform effective population registry for long‐term health monitoring; and v)service the displaced population on day‐to‐day needs. Results: Comparison of current planning versus plans from our system shows a 5‐fold efficiency improvement. This translates to more people being screened and decontaminated within limited time and resources and thus improve safety and health monitoring for the affected population. Further, workers are more confident and operations are smoother and more organized, thus ensuring public confidence and team‐morale. Conclusions: The system has real‐time computation capability and can be used by emergency management administrators for actual strategic and operational planning and execution; to educate and train current and future personnel on decision making under uncertainties; and to simulate responses to catastrophic events through systematic analysis of numerous scenarios, including worst‐case, to learn of erratic as well as efficient response strategies. Real‐time data‐feeds allow re‐configuration on‐the‐fly as the event unfolds. National Science Foundation
Purpose: The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor‐control‐probability(TCP) with an acceptable normal‐tissue‐complication probability(NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. We design treatment plans that optimize TCP directly and contrast them with the clinical dose‐based plans. PET image is incorporated to evaluate gain in TCP for dose escalation. Methods: We build a nonlinear mixed integer programming optimization model that maximizes TCP directly while satisfying the dose requirements on the targeted organ and healthy tissues. The solution strategy first fits the TCP function with a piecewise‐linear approximation, then solves the problem that maximizes the piecewise linear approximation of TCP, and finally performs a local neighborhood search to improve the TCP value. To gauge the feasibility, characteristics, and potential benefit of PET‐image guided dose escalation, initial validation consists of fifteen cervical cancer HDR patient cases. These patients have all received prior 45Gy of external radiation dose. For both escalated strategies, we consider 35Gy PTV‐dose, and two variations (37Gy‐boost to BTV vs 40Gy‐boost) to PET‐image‐pockets. Results: TCP for standard clinical plans range from 59.4% ‐ 63.6%. TCP for dose‐based PET‐guided escalated‐dose‐plan ranges from 63.8%–98.6% for all patients; whereas TCP‐optimized plans achieves over 91% for all patients. There is marginal difference in TCP among those with 37Gy‐boosted vs 40Gy‐boosted. There is no increase in rectum and bladder dose among all plans. Conclusion: Optimizing TCP directly results in highly conformed treatment plans. The TCP‐optimized plan is individualized based on the biological PET‐image of the patients. The TCP‐optimization framework is generalizable and has been applied successfully to other external‐beam delivery modalities. A clinical trial is on‐going to gauge the clinical significance. Partially supported by the National Science Foundation.
Purpose: Intensity‐modulated treatment planning methods have not previously accounted for systematic errors in tumor and tissue locations. We introduce a novel method for including systematic errors during the optimization phase, based on estimated probabilities of systematic geometrical shifts or tissue misidentification(i.e.tumor location). Method and Materials: Given a planning geometry, the users supply estimates of the systematic probability that tissues are actually displaced by a given vector(“ensemble”). Overall plan quality is based on probability‐weighted measures of actual plan realization. Ensembles are estimated here based on dose‐convolution with Gaussian probability distributions. Two different objective function metrics based on this paradigm are investigated:the maximum probability of tumor under‐dosage is computed using integer variables which divide dose values into under‐dose vs. adequate dose. Similarly, the probability that dose to a critical serial normal structure will exceed tolerance is formed into an objective function. A third metric is the uncertainty in the mean dose given to the tumor, averaged over ensembles. Both of these can be mixed with other standard metrics in the IMRT treatment planning optimization process. Results: Mathematical formulas for the metrics were derived. We demonstrate the use of ensemble averaging using large‐scale mixed integer programming. A systematic setup shift Gaussian probability distribution of 5 mm(half‐width) was assumed. A lung treatment plan was tested partly based on minimizing the highest probability of clinical target volume under‐dosage. The plan was more robust against systematic errors than the comparable plan without considering the robust metric. Conclusion: Systematic uncertainties can be considered directly within the optimization process by averaging over ensembles. The result is increased robustness of the treatment plans. The method of estimating dose to the ensemble treatment plans needs to be studied to potentially improve its accuracy. We have introduced novel metric functions based on explicitly considering the effects of systematic errors, thereby resulting in treatment plans of increased robustness.
Purpose: We present a multiple‐objective treatment planning framework that involves a wide range of treatment planning variables and multiple clinical objectives and constraints. Our goal is to compare the quality of resulting treatment plans from a multiple‐objective approach versus plans from current clinical systems. The techniques is applied across two clinical sites (thus with different clinician preferences and outcome endpoints) to observe consistency of the proposed methods. Methods and Materials:We combine preemptive programming and weighted sum approaches within a large‐scale discrete optimization framework to manage the multiple clinical objectives. Four objectives are optimized explicitly: PTV homogeneity, conformity, OAR doses and OAR DVHs. Elements of uncertainty in constraints (e.g., best possible limiting dose bounds to OARs) are preemptively determined and adaptively incorporated into the treatment planning model. Results:Compared to clinical plans, for a collection of 10 head‐and‐neck cases from 2 clinics, PTV homogeneity improves by 5–14% when using the multiple objective approach, whereas conformity improves by 2–5%. The mean‐dose to organs‐at‐risks reduces uniformly by 8–42% for parotid, 6–30% for mandible, 10–28% for larynx, and 7–22% for oral cavity. In particular, all critical structures receive significantly less dose, as the multiple objective approach focuses the radiation onto the tumor volume. The plans provide over 95% PTV coverage, as requested by the clinicians. Conclusion:The multiple‐objective approach produces very high‐quality plans. Although the resulting solution is sensitive with respect to the order the objectives are optimized and prioritized, across the two clinical sties, the multiple objective schema works consistently well. Specifically, at both sites, the multiple objective approach results in plans with drastic dose reduction to critical structures, while simultaneously improving PTV coverage, conformity, and dose homogeneity when compared to plans obtained via commercial planning systems. Elements of uncertainty in constraints can be preemptively determined. Computationally it takes about an hour to generate each plan.
Purpose: Although surgery remains the treatment of choice, patients with carcinoma of the extrahepatic bile duct who are not candidates for surgical resection(∼75%) are treated palliatively with radiation therapy and/or chemotherapy. Intraluminal LDR or HDR brachytherapy has the well‐documented advantage of delivering a large target dose, while significantly sparing surrounding healthy tissues. Typically, treatment planning is performed with respect to source‐guide catheter(s) inserted along the biliary canal under fluoroscopic guidance. The prescription hull refers to a volume at a distance of 0.5–1.0 cm from catheter(s). Because of the irregular shape of the source guides, manual contouring of the prescription hull is time consuming and subject to errors. We describe, and illustrate with actual treatment plans, an automatic algorithm to outline the planning volume. Methods and Materials: The contouring of a single catheter can be considered as the trajectory generated by a moving ball. The path of the ball forms a guide curve, which is given by the digitized‐coordinates of the source‐guide catheter. Given a radius r that defines the thickness of the PTV, our volume‐definition algorithm applies curve fitting via Ferguson spline interpolation, followed by circle generation and sphere generation. For multiple catheters, the algorithm is more complex, requiring curve fitting, warping, slicing, and linking to obtain the resulting tumor shape. Results: For the biliary intraluminal case, the algorithm takes only seconds to generate a 3D‐planning volume. Applying it to complex hypothetical cases reveals that it handles irregular tumor volumes and shapes well, and returns a 3D‐planning volume within 1 CPU‐minute. Conclusion: The automatic volume‐definition algorithm works reliably and quickly, and results in plans that are demonstratively superior to those obtained by manual planning. Furthermore, the planning volume is a necessary input to computer‐based plan optimization. The volume‐definition algorithm improves the treatment dosimetric conformity, a factor that may contribute to improved clinical outcome.
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