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The authors developed a model that incorporates two mathematical innovations for designing a personalized treatment plan tailored specifically to a diabetes patient’s unique dose-effect characteristics.

To determine whether a collaborative learning strategy-derived clinical practice guideline can reduce the duration of endotracheal intubation following infant heart surgery.Prospective and retrospective data collected from the Pediatric Heart Network in the 12 months pre- and post-clinical practice guideline implementation at the four sites participating in the collaborative (active sites) compared with data from five Pediatric Heart Network centers not participating in collaborative learning (control sites).Ten children's hospitals.Data were collected for infants following two-index operations: 1) repair of isolated coarctation of the aorta (birth to 365 d) and 2) repair of tetralogy of Fallot (29-365 d). There were 240 subjects eligible for the clinical practice guideline at active sites and 259 subjects at control sites.Development and application of early extubation clinical practice guideline.After clinical practice guideline implementation, the rate of early extubation at active sites increased significantly from 11.7% to 66.9% (p0.001) with no increase in reintubation rate. The median duration of postoperative intubation among active sites decreased from 21.2 to 4.5 hours (p0.001). No statistically significant change in early extubation rates was found in the control sites 11.7% to 13.7% (p = 0.63). At active sites, clinical practice guideline implementation had no statistically significant impact on median ICU length of stay (71.9 hr pre- vs 69.2 hr postimplementation; p = 0.29) for the entire cohort. There was a trend toward shorter ICU length of stay in the tetralogy of Fallot subgroup (71.6 hr pre- vs 54.2 hr postimplementation, p = 0.068).A collaborative learning strategy designed clinical practice guideline significantly increased the rate of early extubation with no change in the rate of reintubation. The early extubation clinical practice guideline did not significantly change postoperative ICU length of stay.

Public health experts agree that the best strategy to contain a pandemic, where vaccination is the prophylactic treatment but vaccine supply is limited, is to give higher priority to higher-risk populations. We derive a mathematical decision framework to track the effectiveness of prioritized vaccination through the course of a pandemic. Our approach couples a disease-propagation model with a vaccine queueing model and an optimization engine to determine optimal prioritized coverage in a mixed-vaccination strategy. This demonstrably minimizes infection and mortality. Given estimated outbreak characteristics, vaccine inventory levels, and individual risk factors, the study reveals an optimal coverage for the high-risk group that results in the lowest overall attack and mortality rates. This knowledge is critical to public health policy makers for determining the best strategies for population protection. This becomes particularly important in determining when to switch from a prioritized strategy emphasizing high-risk groups to a nonprioritized strategy in which the vaccine becomes available publicly. Our analysis highlights the importance of uninterrupted vaccine supply. Although the 2009 H1N1 supply, received in interrupted batches, eventually covered over 30 percent of the population, the resulting attack and mortality rates are significantly inferior to those in a scenario where only 20 percent of the population is covered with an uninterrupted supply. We also learned that early vaccination is important. Contrasting the 2009 H1N1 supply to a 10 percent uninterrupted supply, a three-week delay in vaccination reduces the 9.9 percent infection reduction of the former to a mere 0.9 percent. The optimal trigger for switching from prioritized to nonprioritized vaccination is sensitive to infectivity and vulnerability of the high-risk groups. Our study further underscores the importance of throughput efficiency in dispensing and its effects on the overall attack and mortality rates. The more transmissible the virus is, the lower the threshold for switching to nonprioritized vaccination. Our model, which can be generalized, allows the incorporation of seasonality and virus mutation of the biological agents. The system empowers policy makers to make the right decisions at the appropriate time to save more lives, better utilize limited resources, and reduce the health-service burden during a pandemic event.

This study focuses on reducing SSI for coronary artery bypass graft(CABG) patients at a large safety-net hospital. The SSI rate in 2010–11 hovered around 20%. The overall goal is to reduce CABG-SSI incidence by 25% (to < 15%) and measure its sustainability. A system-approach is employed which takes into account the interdependency of preoperative, intraoperative and postoperative processes. A decision tree model and a simulation-optimization model are developed to identify critical infection factors. Implemented changes involve pre-op sterilization, aggressive nasal cleaning, proper hair-clipping, and optimized antibiotics prophylaxis timing and dosage. E-alerts are also implemented for documentation to facilitate compliance and training. The site realized a drop of 65% in SSI (from 23% to 8%) in the first six months. It achieved zero percentage thereafter and sustained that rate for 18 months. The system-approach is generalizable and is currently being explored for rectal-colon cancer and hysterectomy, the second most common elective surgery among American women.

Cervical cancer has a high mortality rate (approximately 35 percent) in the United States and is difficult to treat successfully. One promising treatment is high-dose-rate brachytherapy, which entails delivering high-dose radiation to the tumor via the temporary implantation of radioactive seeds. This treatment promises to be particularly effective in eradicating tumors while preserving the organs. Yet, major obstacles to successful treatment remain, especially (1) determining the best seed type, spatial configuration of seeds, and seed dwelling time, and (2) improving the probability that the treatment will eliminate all malignant cells. We developed an advanced planning model to simultaneously address both of these issues. To permit taking advantage of the best available information, our model works with inputs from positron emission tomography. We begin with a multiobjective, nonlinear, mixed-integer programming model that is initially intractable. To solve the model, we introduce an original branch-and-cut and local-search approach that couples new polyhedral cuts with matrix reduction and intelligent geometric heuristics. The result has been accurate solutions that are obtained rapidly. Clinical trials at Rush University Medical Center demonstrated superior medical outcomes. These analytical techniques are applicable not only to cervical cancer, but also to other types of cancer, including breast, lung, and prostate cancer.

Planning for a catastrophe involving a disease outbreak with the potential for mass casualties is a significant challenge for emergency managers. Public health experts at the US Centers for Disease Control and Prevention (CDC) teamed with operations researchers to address important aspects of mass dispensing: medical supply distribution, locations of dispensing facilities, optimal facility staffing and resource allocation, routing of the population, and dispensing methods. Simulation-optimization technology was integrated into a decision support and data management suite, RealOpt©, for tactical and strategic operational planning. RealOpt has enabled the CDC to provide modern tools that support dynamic planning for emergencies and that establish a knowledge data bank to provide feedback about the deployment of various techniques. The RealOpt suite now has a US user base of over 6,500 public health and emergency directors covering all states, plus many international users. RealOpt has been applied in hundreds of drills and dispensing events, including anthrax preparedness, and for seasonal flu and H1N1 vaccination events.

Background Collaborative learning is a technique through which individuals or teams learn together by capitalizing on one another's knowledge, skills, resources, experience, and ideas. Clinicians providing congenital cardiac care may benefit from collaborative learning given the complexity of the patient population and team approach to patient care. Rationale and development Industrial system engineers first performed broad-based time-motion and process analyses of congenital cardiac care programs at 5 Pediatric Heart Network core centers. Rotating multidisciplinary team site visits to each center were completed to facilitate deep learning and information exchange. Through monthly conference calls and an in-person meeting, we determined that duration of mechanical ventilation following infant cardiac surgery was one key variation that could impact a number of clinical outcomes. This was underscored by one participating center's practice of early extubation in the majority of its patients. A consensus clinical practice guideline using collaborative learning was developed and implemented by multidisciplinary teams from the same 5 centers. The 1-year prospective initiative was completed in May 2015, and data analysis is under way. Conclusion Collaborative learning that uses multidisciplinary team site visits and information sharing allows for rapid structured fact-finding and dissemination of expertise among institutions. System modeling and machine learning approaches objectively identify and prioritize focused areas for guideline development. The collaborative learning framework can potentially be applied to other components of congenital cardiac care and provide a complement to randomized clinical trials as a method to rapidly inform and improve the care of children with congenital heart disease.

The study aims to reduce the incidence of central line associated bloodstream infections (CLABSI). We design a computer model that comprises the entire process of central line insertion and maintenance. The model attempts to capture all major events in patient care from entrance to the hospital through the time at which the central line is ultimately removed. CLABSI data is analyzed to identify areas of potential increased risk of patient death from CLABSI. Specifically, we attempt to predict death among patients affected with CLABSI while minimizing Type II (false negative) error. By crafting the model to prioritize against this type of error, and thus identifying patients most likely to die, providers will have the best chance of intervening to reduce CLABSI-related deaths. The study led to implementation of reminders and protocols that result in a reduction of 18% of CLABSI over a period of 12 months. The predictive analysis identifies high-risk individuals to allow for proper intervention to prevent CLABSI-related deaths.

Purpose: In current intensity-modulated radiation therapy (IMRT) plan optimization, the focus is on either finding optimal beam angles (or other beam delivery parameters such as field segments, couch angles, gantry angles) or optimal beam intensities. In this article we offer a mixed integer programming (MIP) approach for simultaneously determining an optimal intensity map and optimal beam angles for IMRT delivery. Using this approach, we pursue an experimental study designed to (a) gauge differences in plan quality metrics with respect to different tumor sites and different MIP treatment planning models, and (b) test the concept of critical-normal-tissue-ring —a tissue ring of 5 mm thickness drawn around the planning target volume (PTV)—and its use for designing conformal plans. Methods and Materials: Our treatment planning models use two classes of decision variables to capture the beam configuration and intensities simultaneously. Binary (0/1) variables are used to capture "on" or "off" or "yes" or "no" decisions for each field, and nonnegative continuous variables are used to represent intensities of beamlets. Binary and continuous variables are also used for each voxel to capture dose level and dose deviation from target bounds. Treatment planning models were designed to explicitly incorporate the following planning constraints: (a) upper/lower/mean dose-based constraints, (b) dose–volume and equivalent-uniform-dose (EUD) constraints for critical structures, (c) homogeneity constraints (underdose/overdose) for PTV, (d) coverage constraints for PTV, and (e) maximum number of beams allowed. Within this constrained solution space, five optimization strategies involving clinical objectives were analyzed: optimize total intensity to PTV, optimize total intensity and then optimize conformity, optimize total intensity and then optimize homogeneity, minimize total dose to critical structures, minimize total dose to critical structures and optimize conformity simultaneously. We emphasize that the objectives that include optimizing conformity make use of the critical-normal-tissue-ring. Three tumor sites: head-and-neck, pediatric brain, and prostate are used for comparison. Results: The critical-normal-tissue-ring acts as a good device for enforcing conformity. Trends in the characteristics and quality of plans resulting from each model were observed. Attempts to reduce dose to critical structures tend to worsen PTV conformity (1.542 to 3.092) and homogeneity (1.223 to 1.984), depending on the relative size and spatial distance of the critical structures to the PTV. When the critical structures are relatively small compared with the PTV (as in the case for the pediatric brain tumor, where each is less than 15% in volume), dose reduction to critical structures is accompanied by much worse scores in conformity (2.482) and homogeneity (1.984). When the critical structures are larger, as in the case of head-and-neck (∼50%), the conformity and homogeneity deterioration is less significant (1.542 and 1.239, respectively). There is a clear tradeoff between homogeneity, conformity, and minimum dose to organs at risk (OARs). For head-and-neck and pediatric brain tumor, the model that minimizes total dose to critical structures and optimizes conformity simultaneously offers a compromise among these factors, resulting in reduced critical structure dose with conformal and homogeneous plans. In the prostate case, the tumor is smaller than the two large nearby critical structures, and all models provide very homogeneous PTV dose distribution. However, minimizing dose to critical structures worsens conformity, as it spreads the radiation to the area surrounding the PTV. The maximum dose to the critical structures also increases slightly. Compared with plans used in the clinic which generally have uniformly spaced beam angles, the optimal clinically acceptable plans obtained via the methods herein do not have equispaced beams. The optimal beam angles returned appear to be nonintuitive, and depend on PTV size and geometry and the spatial relationship between the tumor and critical structures. Conclusions: The MIP model described allows simultaneous optimization over the space of beamlet fluence weights and beam and couch angles. Based on experiments with tumor data, this approach can return good plans that are clinically acceptable and practical. This work distinguishes itself from recent IMRT research in several ways. First, in previous methods beam angles are selected before intensity map optimization. Herein, we employ 0/1 variables to model the set of candidate beams, and thereby allow the optimization process itself to select optimal beams. Second, instead of incorporating dose–volume criteria within the objective function as in previous work, herein, a combination of discrete and continuous variables associated with each voxel provides a mechanism to strictly enforce dose–volume criteria within the constraints. Third, using the construct of critical-normal-tissue-ring within the objective function can enhance the achievement of conformal plans. Based on the three tumor sites considered, it appears that volume and spatial geometry with respect to the PTV are important factors to consider when selecting objectives to optimize, and in estimating how well suited a particular model is for achieving a specified goal.

Purpose: In current practice, planning for prostate brachytherapy is based on the state of the prostate at a particular instant in time. Because treatment occurs over an extended period, changes in the prostate volume (gland shrinkage) and seed displacement lead to disagreement between planned dosimetry to the prostate and the dose actually received by the prostate. Discrepancies between planned and actual dose to the rectum and urethra also occur. The purpose of this study is to investigate the possibility of defining an "effective planning volume" that compensates for changes in prostate volume and seed displacement. Methods and Materials: Waterman's formula is used to estimate prostate shrinkage and seed displacement. The prostate volume and potential seed positions at days 0, 6, 12, 18, 24, and 30 are used in formulating time-dependent dosimetric treatment planning models. Both single-period and multi-period models are proposed and analyzed. A state-of-the-art computational engine generates unbiased, high-quality treatment plans in a matter of minutes. Plans are evaluated using coverage and conformity indices computed at specific times over a period of 30 days. The models allow dose to urethra and rectum to be strictly controlled at specific instants in time, or throughout the 30-day horizon. Results: For plans generated from the single-period models—based on projected prostate volumes and potential seed positions on days t = 0, 6, 12, 18, 24, 30, respectively—as t increases, the conformity index improves while the coverage worsens. In particular, the best coverage and worst conformity are achieved for the plan generated using t = 0 (day 0) information. This plan provides over 99% coverage over the entire 30-day period, and while it has initial conformity index 1.24, the conformity index climbs to 1.58 by day 30. Conversely, the worst coverage and best conformity are achieved when the plan is generated using projected information from t = 30 (day 30). Plans based on projected data at day 30 yield an initial coverage of only 84%, with conformity scores less than 1.34 over the entire 30-day period. Among the multi-period plans, with the exception of the two-period plan obtained using day 0 and projected day 6 data, the average coverage is 98% while conformity indices below 1.46 are maintained throughout the 30-day horizon. Excessive dose to the urethra and rectum is observed when only day 0 dosimetric and volumetric data are imposed in the planning procedure. In this case, by day 30, 89% of urethra volume receives dose in excess of 120% of the remaining prescription dose. Similarly, 40% of rectum volume receives dose in excess of the prescribed upper dose bound of 78% of the remaining prescription dose. When multi-period dosimetric constraints for urethra and rectum are imposed, dose to these structures is controlled throughout the 30-day period. Conclusions: A planning method that takes into account prostate shrinkage and seed displacement over time can be used to adjust the balance between coverage and conformity. Incorporating projected future volumetric information is useful in providing more conformal plans, in some cases improving conformity by as much as 21% while sacrificing roughly 7% of initial coverage. Evidence of possible morbidity reduction to urethra and rectum via the use of multi-period dosimetric constraints on these structures is demonstrated. Among all plans considered, the plan obtained via the six-period model provides the best coverage and conformity over the 30-day horizon.

The 0/1 knapsack equality polytope is, by definition, the convex hull of 0/1 solutions of a single linear equation. A special form of this polytope, where the defining linear equation has nonnegative integer coefficients and the number of variables having coefficient one exceeds the right-hand side, is considered. Equality constraints of this form arose in a real-world application of integer programming to a truck dispatching scheduling problem. Families of facet defining inequalities for this polytope are identified, and complete linear inequality representations are obtained for some classes of polytopes.

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.

A catastrophic health event, such as a terrorist attack with a biological agent, a naturally occurring pandemic, or a calamitous meteorological or geological event, could cause tens or hundreds of thousands of casualties, weaken the economy, damage public morale and confidence, create panic and civil unrest, and threaten national security. It is therefore critical to establish a strategic vision that will enable a level of public health and medical preparedness sufficient to address a range of possible disasters. Planning for a catastrophe involving a disease outbreak or mass casualties is an ongoing challenge for first responders and emergency managers. They must make critical decisions on treatment distribution points, staffing levels, impacted populations and potential impact in a compressed window of time when seconds could mean life or death. Some of the key areas of public health and medical preparedness include medical surge, population protection, communication infrastructure, and emergency evacuation. This chapter highlights our own experience on projects with the Centers for Disease Control and Prevention and various public health jurisdictions in emergency response and medical preparedness for mass dispensing for disease prevention and treatment and large-scale disaster relief efforts.

The number of people affected by Alzheimer’s disease is growing at a rapid rate, and the consequent increase in costs will have significant impacts on the world’s economies and health care systems. Therefore, there is an urgent need to identify mechanisms that can provide early detection of the disease to allow for timely intervention. Neuropsychological tests are inexpensive, non-invasive, and can be incorporated within an annual physical examination. Thus they can serve as a baseline for early cognitive impairment or Alzheimer’s disease risk prediction. In this paper, we describe a PSO-DAMIP machine-learning framework for early detection of mild cognitive impairment and Alzheimer’s disease. Using two trials of patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and control groups, we show that one can successfully develop a classification rule based on data from neuropsychological tests to predict AD, MCI, and normal subjects where the blind prediction accuracy is over 90%. Further, our study strongly suggests that raw data of neuropsychological tests have higher potential to predict subjects from AD, MCI, and control groups than pre-processed subtotal score-like features. The classification approach and the results discussed herein offer the potential for development of a clinical decision making tool. Further study must be conducted to validate its clinical significance and its predictive accuracy among various demographic groups and across multiple sites.

Developing and maintaining a robust communication infrastructure among hospitals, emergency medical services, and other health-care facilities, as well as private medical transport companies and medical supplies vendors, is vital to providing relief operations and services during emergency situations. Such infrastructure provides the core foundation for basic knowledge-sharing such as patient volume and severity, emergency room capacity, hospital bed availability; special wards availability; medical personnel specialties; transport vehicle availability; and blood, medicine, and medical supplies inventories. During an emergency situation, such critical information can be communicated to a regional coordinating control center, which can assess available resources, identify surpluses and shortages, and coordinate distribution efforts. In fact, building capacity for an interoperable communication system for emergency response is identified as one of the areas that must be prioritized according to The Hospital Preparedness Program established by the US Department of Health and Human Services. In this article, we highlight some important issues related to an effective communication infrastructure. Various communication tools and infrastructure will be contrasted. Organizational and sociological factors as well as human behavior are discussed. Beyond communicating horizontally and vertically across the jurisdictions and federal, state, and local government agencies, we also highlight the importance of multimedia public information and risk communication factors that are essential for mounting an effective emergency response. Keywords: public health; emergency response; disaster medicine; medical preparedness; homeland security; terrorist attack; emergency communication infrastructure; physical Infrastructure; communication tools; communication infrastructure; communication facilitation programs; organizational factors; sociological factors; human behavior; public information; risk communication

Medical surge forms the cornerstone of preparedness planning efforts for major medical incidents. Surge capacity encompasses multidimensional aspects such as potential patient beds; available space in which patients may be triaged, managed, vaccinated, decontaminated, or simply located; available care personnel of all types; necessary medications, supplies, and equipment; and even the legal capacity to deliver health care under situations that exceed authorized capacity. Further, close collaboration between public health and health care facilities is very important to effective disaster response. In the aftermath of the September 11 terrorist attacks and the dissemination of anthrax in 2001, the ability of the US health care system to provide an effective and coordinated response to mass casualty or complex incidents came under intense scrutiny. More recently, the devastation caused by Hurricane Katrina and the mass disruption of public health and medical services along the Gulf Coast spotlighted the need for cohesive strategies that focus on management systems for major public health and medical response. In this article, we explore hospital surge capacity planning. Specifically, we will discuss issues related to surge patterns, the interplay of care personnel and staff, facilities, equipment and supplies, and incident management system. Approaches used in balancing daily operational efficiency versus maintaining an appropriate surge capacity will be discussed, and the importance of international collaboration is highlighted. Further, OR methodologies, including simulation, optimization, stochastic processes and systems dynamics models used in medical surge analysis will be briefly reviewed. Keywords: public health; emergency response; disaster medicine; medical preparedness; homeland security; terrorist attack; hospital surge capacity; surge patterns; incident management system; international collaboration

In the aftermath of the September 11 terrorist attacks and the dissemination of anthrax in 2001, the ability of the US health-care system to provide an effective and coordinated response and evacuation capability to mitigate mass casualty or complex incidents came under intense scrutiny. More recently, the devastation caused by Hurricane Katrina and the mass disruption of public health and medical services along the Gulf Coast spotlighted the need for cohesive strategies that focus on mounting effective evacuation and response for catastrophic events. Determining the most effective evacuation plan for a large public facility requires in-depth analysis of multiple interdependent and competing factors. Determining the best routes, foreseeing potential problems, addressing the chaos/panic factor, and orchestrating the evacuation are all critical aspects that should be evaluated in a well-developed disaster management plan. The success of the evacuation of 80,000 in the stadium Santiago Bernabeu in Madrid, Spain within 7 min sheds some light on the potential to save many lives through careful planning and proper evacuation responses. Emergency responders and coordinators must be equipped with such knowledge so as to protect the regional population during critical incidents. In this article, we seek to understand the elements that influence evacuation planning, issues faced by decision makers, as well as methodologies from the field of operations research that are leading to more effective planning and execution. Keywords: public health; emergency response; disaster medicine; medical preparedness; homeland security; terrorist attack; emergency evacuation; evacuation behavior; interdependencies (facility, people, support services); simulation models; optimization; computational intractability

A catastrophic health event, such as a terrorist attack with a biological agent, a naturally occurring pandemic, or a calamitous meteorological or geological event, could cause tens or hundreds of thousands of casualties, weaken the economy, damage public morale and confidence, create panic and civil unrest, and threaten national security. It is therefore critical to establish a strategic vision that will enable a level of public health and medical preparedness sufficient to address a range of possible disasters. Planning for a catastrophe involving a disease outbreak or mass casualties is an ongoing challenge for first responders and emergency managers. They must make critical decisions on treatment distribution points, staffing levels, impacted populations, and potential impact in a compressed window of time when seconds could mean life or death. Some of the key areas of public health and medical preparedness include medical surge, population protection, communication infrastructure, and emergency evacuation. This article highlights our own experience on projects with the Centers for Disease Control and Prevention and various public health jurisdictions in emergency response and medical preparedness for mass dispensing for disease prevention and treatment, and large-scale disaster relief efforts. Keywords: public health; emergency response; disaster medicine; medical preparedness; homeland security; terrorist attack; medical countermeasures dispensing; disaster relief; mode of dispensing; point of dispensing; facility location; layout design; resource allocation; supply chain and management; risk communication and public information; media exposure; pandemic; anthrax; flu

To assess whether independent evaluation of coronal and sagittal reformatted images can replace axial images for primary interpretation of multidetector row computed tomography (MDCT) images in patients with thoraco-abdominal trauma. 111 (M/F 69:42) patients with acute chest or abdominal trauma underwent 16-channel MDCT. Coronal and sagittal multiplanar reformatted (MPR) images were generated from thin-section axial images. Two radiologists independently interpreted the MPR images first followed by axial images for both imaging findings as well as adequacy of image quality. Differences between independent reader review of axial and MPR images were assessed using the Wilcoxon signed rank test. There was no significant difference in soft tissue findings identified on MPR vs axial images for either reader, p = 0.91 and 0.34, respectively. However, both readers identified more skeletal findings on the MPR as compared to the axial images, p = 0.026 and 0.040, respectively. There was no significant difference between the readers in their interpretation of axial (soft tissue, p = 0.56; skeletal, p = 0.65) or MPR (soft tissue, p = 0.32; skeletal, p = 0.65) findings. More skeletal findings were identified during the isolated review of MPR as compared to axial images alone. However, the use of MPR images alone still resulted in an unacceptably high number of missed soft tissue and even skeletal findings. A combined approach where both data sets are simultaneously available is therefore preferred.

This chapter describes treatment planning optimization in brachytherapy and the design of a clinical decision support system. Brachytherapy refers to the placement of radioactive sources (seeds) inside a tumor site. The fundamental problem in treatment planning for brachytherapy is to determine where to place sources so as to deliver a sufficient radiation dose to kill the cancer, while limiting exposure of healthy tissue. We first present the sequence of steps that are involved in brachytherapy treatment planning. State-of-the-art mixed integer programming models are then described and some algorithmic approaches are outlined. The automated clinical decision support system allows for real-time generation of optimal seed configurations using ultrasound images acquired prior to seed implantation, and dynamic dose correction during the implantation process.

Background Allograft vasculopathy remains a major limiting factor in long-term graft survival. The absence of symptoms and diffuse nature of the disease make clinical detection and therapy more difficult. Limited data exist on the long-term outcome of percutaneous interventions in this group of patients. Methods Medical records and cardiac catheterizations from the Cardiac Cath Lab database were retrospectively reviewed for all cardiac transplant recipients who had undergone a percutaneous intervention. Procedural results, complications, use of stents and angiographic follow-up were recorded. Re-stenosis was defined as a lesion >50% in the target vessel at follow-up angiography. Results Thirty-three patients underwent 97 percutaneous interventions with a mean of 2.9 interventions per patient. Mean age at the time of first intervention was 52 ± 13 (mean ± standard deviation) years. Mean time from transplant to first intervention was 5 ± 3.0 years. The primary procedural success rate was 99%. Thirty-four procedures involved placement of a stent, 63 were angioplasty alone. There were no procedure-related complications. Seventy percent of lesions were de novo and 30% were re-stenotic lesions. Six-month, 12-month and 5-year target vessel re-stenosis rates in the stent group were 31%, 46% and 69%, and in the percutaneous transluminal coronary angioplasty (PTCA) group were 41%, 53% and 68%, respectively. Thirteen patients (39.3%) died or were re-transplanted, at 1.9 ± 2.29 (mean ± SD) years after their first intervention. Twenty patients were alive at 4.5 ± 2.99 years after the first intervention. Conclusions Percutaneous intervention can be performed safely in cardiac transplant recipients. Stent placement reduces early and mid-term re-stenosis, but late re-stenosis occurs in 70% of lesions. Late re-stenosis, development of new coronary lesions, and need for repeat intervention are common, regardless of the method used for percutaneous intervention, emphasizing the diffuse and progressive nature of transplant coronary disease.

Purpose With the advent of intraoperative optimized planning, the treatment of prostate cancer with permanent implants has reached an unprecedented level of dose conformity. However, because of well-documented (and unavoidable) inaccuracies in seed placement into the gland, carrying out a plan results in a large degree of variability relative to the intended dose distribution. This brings forth the need to periodically readjust the plan to allow for the real positions of seeds already implanted. In this paper, an algorithm for performing this task, hereby described as intraoperative dynamic dose optimization (IDDO), is presented and assessed. Methods and materials The general scheme for performing IDDO consists of three steps: ( 1 ) at some point during the implant, coordinates of implanted seeds are identified; ( 2 ) seed images are projected onto the reference frame of the ultrasound images for planning; and ( 3 ) the plan is reoptimized. Work on the first two steps is reported elsewhere. Here, we focus on the strategy for implementing the reoptimization step. An optimal treatment plan is first obtained based on initial operating room–acquired ultrasound images. We analyze the sensitivity and effect of the IDDO procedure with respect to the total number of reoptimizations performed. Specifically, we consider reoptimizing 2, 3, and 4 times. When two reoptimizations are used, half of the seeds from the initial optimal plan are implanted. The first reoptimization is performed on the remaining possible seed positions, and all the seeds designated in this reoptimized plan are implanted. The second (final) reoptimization is done on the remaining unused seed positions to ensure 100% coverage of the gland and to eliminate possible cold spots in the gland. Similarly, when three reoptimization steps are used, one-third of the seeds from the initial optimized plan, one-half of the seeds from the first reoptimization, and all seeds from the second reoptimization are implanted. The third (final) reoptimization is performed to assist in eliminating possible cold spots. Reoptimizing four times proceeds in a like manner. Fifteen patient cases are used for comparison. Strict dose bounds of 100% and 120% of the prescription dose are imposed on the urethra, and 100% coverage is imposed on the prostate volume. To assist in achieving good conformity, prostate contour points are assigned a target upper dose bound of 150% of the prescription dose. Results A two-way comparison is performed: (a) initial optimized plan, (b) IDDO plan. Postimplant dose analysis, coverage and conformity measures, as well as actual dose received by urethra and rectum are used to gauge the results. The initial optimized plan consistently provides 93% prescription dose coverage to the gland with average conformity index of 1.32. The urethra dose ranges within 100% to 150%, and the maximum dose delivered to the rectum reaches 91% of the prescription dose. On average, about 50% of the urethra receives more than 120% of the prescription dose, and 19% of the rectum volume receives more than the 78% upper dose limit. For the IDDO plan, 100% postimplant coverage with 1.16 conformity is achieved. Urethra and rectum dose is maintained within the prescribed 100% to 120% range and 78% upper bound, respectively. Conclusions With real-time treatment planning, it is possible to dynamically reoptimize treatment plans to account for actual seed positions (as opposed to planned positions) and needle-induced swelling to the gland during implantation. Postimplant analysis shows that the final seed configuration resulting from the IDDO method yields improved dosimetry. The algorithmic design ensures that one can achieve complete coverage while maintaining good conformity, thus sparing excess radiation to external tissue. The study also provides evidence of the possibility of morbidity reduction to urethra and rectum (because of reduced dose delivered to these structures) via the use of IDDO planning. Clinical studies are needed to validate the importance of our approach.

Treatment planning for prostate brachytherapy is guided largely by the desire to determine a seed configuration that yields a prescription isodose surface that conforms with the prostate boundary. Currently, planning is based on the state of the prostate at a particular instant in time; typically, when the prostate is swollen. Unfortunately, a plan that conforms to the swollen prostate will not match the gland when shrinkage and seed displacement occur. Furthermore, these factors may directly affect the dose received by urethra and rectum over an extended period of time. The authors examine the possibility of defining an "effective planning volume" (EPV) that compensates for information that is unavailable during initial planning (e.g. changes in prostate volume, seed displacement).

In current practice, the planning volume for adjuvant brachytherapy treatment for soft-tissue sarcoma is either not determined a priori (in this case, seed locations are selected based on isodose curves conforming to a visual estimate of the planning volume), or it is derived via a tedious manual process. In either case, the process is subjective and time consuming, and is highly dependent on the human planner. The focus of the work described herein involves the development of an automated contouring algorithm to outline the planning volume. Such an automatic procedure will save time and provide a consistent and objective method for determining planning volumes. In addition, a definitive representation of the planning volume will allow for sophisticated brachytherapy treatment planning approaches to be applied when designing treatment plans, so as to maximize local tumour control and minimize normal tissue complications. An automated tumour volume contouring algorithm is developed utilizing computational geometry and numerical interpolation techniques in conjunction with an artificial intelligence method. The target volume is defined to be the slab of tissue r cm perpendicularly away from the curvilinear plane defined by the mesh of catheters. We assume that if adjacent catheters are over 2r cm apart, the tissue between the two catheters is part of the tumour bed. Input data consist of the digitized coordinates of the catheter positions in each of several cross-sectional slices of the tumour bed, and the estimated distance r from the catheters to the tumour surface. Mathematically, one can view the planning volume as the volume enclosed within a minimal smoothly-connected surface which contains a set of circles, each circle centred at a given catheter position in a given cross-sectional slice. The algorithm performs local interpolation on consecutive triplets of circles. The effectiveness of the algorithm is evaluated based on its performance on a collection of soft-tissue sarcoma tumour beds within various anatomical structures. For each of 15 patient cases considered, the algorithm takes approximately 2 min to generate the planning volume. Although the tumour shapes are rather different, the algorithm consistently generates planning volumes that visually demonstrate smooth curves compactly encapsulating the circles. This general-purpose contouring algorithm works well whether the catheters are all close together, spread far apart in the plane or arranged in a convoluted way. The automatic contouring algorithm significantly reduces labour time and provides a consistent and objective method for determining planning volumes for soft-tissue sarcoma. Further studies are needed to validate the significance of the resulting planning volumes in designing treatment plans and the role that sophisticated brachytherapy treatment planning optimization may have in producing good plans.

The management of breathing‐registered IMRTtreatment planning is explored by direct incorporation of 4D images within the planning process. The movement of the voxels from one CT timeframe to another is “tracked” and modeled. A timestamp for each voxel is used to specify its position throughout the breathing cycle. A single treatment model incorporates planning constraints throughout multiple time periods. Robustness of the algorithm, plan quality, and potential clinical significance are evaluated. 4D‐CT scans of lung/liver cancer patients were acquired with different breathing phases(phases 0–9, 0:full‐inhale, 5:full‐exhale), Three treatment planning strategies are performed and compared. 1)Standard planning with a static PTV based on a single selected phase (control). 2)The Internal Target Volume (ITV) approach, where ITV is defined as the union of CTVs in all breathing phases. 3)Single‐stage‐4D‐image‐guided planning, where within a single treatment optimization model, planning constraints are incorporated on each voxel for each phase throughout the multiple‐phase period. Sophisticated computational optimization techniques are used to solve these models. For both lung/liver cases, the static‐PTV‐plan results in unacceptable PTV‐underdose. Compared to ITV‐pamns, 4D‐image‐guided‐plans offer good coverage and comparable min‐PTV‐dose; while in lung, it reduces normal‐lung‐mean‐dose by 20%, heart‐mean‐dose by 20%, and esophagus‐max‐dose by 15%; and in liver, it reduces normal‐liver mean‐dose by 15% and other normal‐tissue mean‐dose by 20%, with improved PTV‐conformity of 10%. 4D‐Image‐Guided treatment planningoptimization can provide good PTV‐coverage plans, improve PTV‐underdose, and significantly reduce dose to organs‐at‐risk, especially those organs in the proximity of the tumor. Evidence of morbidity reduction to organs‐at‐risk is observed. The challenge involves the ability to solve a large‐scale treatment planning problem. With sophisticated mathematical optimization modeling and computational strategies, such planning is possible and can be made available for clinical use. Clinical studies are needed to validate the importance of our approach to treatment outcome.

Purpose/Objective(s): Tumor control probability (TCP) measures the probability that no malignant cells are left in the affected organ. It is thus an important clinical metric for measuring treatment success. Zaider and Minerbo provide a complex yet important TCP formalism which links the treatment duration, dose deposit, radiobiology of tumor cells and cell life characteristics. We perform feasibility tests on TCP-driven biological planning for high dose rate brachytherapy (HDR) where the objective maximizes TCP of the resulting plans. Robustness of the planning is tested, and plan quality and potential outcome significance are evaluated. Materials/Methods: CT and 18F-fluorodeoxyglucose based-PET images are obtained for 15 cervical cancer patients. All received prior external beam radiation. The CTV prescribed dose is 5 Gy for 4 fractionations, with the PET-identified pockets prescribed an escalated dose ranging from 7.59.0 Gy per fraction. The treatment model determines the dwell time and seed location that maximizes TCP while constraining PTV coverage, the lower/upper dose, and dose-volume shape for organs-at-risk and PTV. Two alternative plans are contrasted: standard HDR plan and PET-pocket escalated plan. Results: TCP for standard plans ranges from 48-63% whereas TCP for PET-guided escalated plans ranges from 82-99%. The increase is most significant with the smallest PET-identified pockets. Uniformly, bladder and rectum receive 5-8% reduced dose. There is a marginal difference in PTV dose between the standard and escalated plans. All resulting TCPdriven plans are clinically acceptable. Conclusions: TCP can be a very important objective for treatment plan optimization. With advances in biological/functional imaging, there is an urgent need to incorporate radiobiological parameters and TCP within the planning process. This study marks the first use of TCP as the driving objective for treatment planning. Although the optimization problem is very difficult to solve and requires computational breakthroughs, the resulting plans improve significantly the overall local tumor control, and also reduce dose to organs at risk. Rush University began clinical studies in July 2011. A long term outcome study must be carried out to gauge the overall impact. Author Disclosure: E.K. Lee: None. F. Yuan: None. A. Templeton: None. R. Yao: None. K. Kiel: None. J. Chu: None.

Purpose: Tumor control probability(TCP) measures the probability that no malignant cells are left in the affected organ. It is an important clinical metric for measuring treatment success. Zaider and Minerbo provide a complex yet important TCP formalism which links the treatment duration, dose deposit, radiobiology of tumor cells and cell life characteristics. We perform feasibility tests on TCP‐driven biological planning for high‐dose‐rate brachytherapy(HDR) where the objective maximizes TCP of the treatment plans. Plan robustness is tested, and plan quality and potential outcome significance are evaluated. Methods: CT and 18F‐fluorodeoxyglucose based‐PET images are obtained for 15 cervical cancer patients. All received prior external‐beam radiation. The CTV prescribed dose is 5Gy for 4 fractionations, with the PET‐identified pockets prescribed an escalated‐dose ranging from 7.5–9.0Gy per fraction. The treatment model determines the dwell time and seed location that maximizes TCP while constraining PTV coverage, the lower/upper dose, and dose‐volume shape for organs‐at‐risk and PTV. We contrast the standard HDR plan and PET‐pocket escalated plan. Results: TCP ranges from 48–63% for standard plans versus 82–99% for PET‐guided escalated plans. The increase is most significant with the smallest PET‐identified pockets. Uniformly, urethra and rectum receive 5–8% reduced dose. There is marginal difference in PTV dose between standard and escalated plans. All resulting TCP‐driven plans are clinically acceptable. Conclusion: TCP can be a very important objective for treatment plan optimization. With advances in biological/functional imaging, there is an urgent need to incorporate radiobiological parameters and TCP within planning. This study marks the first use of TCP as the driving objective for treatment planning. The optimization problem is very difficult to solve and requires computational breakthroughs. The resulting plans improve significantly the overall local tumor control, and reduce organs‐at‐risk dose. Rush University began clinical studies in 2011. A long term outcome study must be carried out to gauge the overall impact.

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.

Purpose: The management of breathing-registered IMRT treatment planning is explored by incorporating the motion pattern of breathing directly into the treatment planning process. The movement of the voxels from one CT timeframe to another is “tracked” and modeled. A timestamp for each voxel specifies its position throughout the breathing cycle. The treatment models incorporate planning constraints throughout multiple time periods. Robustness of the algorithm, plan quality, and potential clinical significance are evaluated. Method and Materials: 4D-CT scans of lung cancer patients were acquired with different breathing phases(phases 0–9, 0:full-inhale, 5:full-exhale), Three treatment planning strategies are performed and compared. 1)Standard planning with a static PTV based on a single selected phase(control). 2)The Internal Target Volume(ITV) approach, where ITV is defined as the union of CTVs in all breathing phases. 3)Multi-stage optimization and planning, where movements of voxels from one CT-timeframe to another are “tracked” and modeled, and planning constraints are incorporated throughout the multiple-phase period. Sophisticated computational optimization techniques are used to solve these models. Results: Applied to a lung case, the static-PTV plan results in unacceptable PTV-underdose. Multistage-plans offer as good coverage as the ITV-plans, comparable minimum PTV dose while simultaneously reducing the mean-dose to left lung normal tissue(20%), heart(20%), and esophagus. Conclusion: Multistage treatment planning optimization can provide good PTV-coverage plans, improve PTV-underdose, and significantly reduce dose to organs-at-risk, especially those organs in the proximity of the tumor. Evidence of morbidity reduction to organs-at-risk are observed. The challenge involves the ability to solve a large-scale treatment planning problem. With sophisticated mathematical optimization modeling and computational strategies, such planning is possible and can be made available for clinical use. Further investigation of ITV will be conducted to understand if improved plan quality can be achieved via improved construction of this structure. Clinical studies are needed to validate the importance of our approach to treatment outcome.

Introduction CSF levels of established Alzheimer’s disease (AD) biomarkers remain stable despite disease progression, and non-amyloid non-tau biomarkers have the potential of informing disease stage and progression. We previously identified complement 3 (C3) to be decreased in AD dementia, but this change was not found by others in earlier AD stages. We hypothesized that levels of C3 and associated factor H (FH) can potentially distinguish between mild cognitive impairment (MCI) and dementia stages of AD, but we also found their levels to be influenced by age and disease status. Results We developed a biochemical/bioinformatics pipeline to optimize the handling of complex interactions between variables in validating biochemical markers of disease. We used data from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI, n = 230) to build parallel machine learning models, and objectively tested the models in a test cohort (n = 73) of MCI and mild AD patients independently recruited from Emory University. Whereas models incorporating age, gender, APOE ε4 status, and CSF amyloid and tau levels failed to reliably distinguish between MCI and mild AD in ADNI, introduction of CSF C3 and FH levels reproducibly improved the distinction between the two AD stages in ADNI (p