Bio-Inspired Strategies for Optimizing Radiation Therapy under Uncertainties

Radiation therapy is a critical component of cancer treatment. However, the delivery of radiation poses inherent challenges, particularly in minimizing radiation exposure to healthy organs surrounding the tumor site. One significant contributing factor to this challenge is the patient's respiration, which introduces uncertainties in the precise targeting of radiation. Managing these uncertainties during radiotherapy is essential to ensure effective tumor treatment while minimizing the adverse effects on healthy tissues. This research addresses the crucial objective of achieving a balanced dose distribution during radiation therapy under conditions of respiration uncertainty. To tackle this issue, we begin by developing a motion uncertainty model employing probability density functions that characterize breathing motion patterns. This model forms the foundation for our efforts to optimize radiation dose delivery. Next, we employ three bio-inspired optimization techniques: Cuckoo search optimization (CSO), flower pollination algorithm (FPA), and bat search Optimization (BSO). Our research evaluates the dose distribution in Gy on both the tumor and healthy organs by applying these bio-inspired optimization methods to identify the most effective approach. This research ultimately aids in refining the strategies used in radiation therapy planning under the challenging conditions posed by respiration uncertainty. Through the application of bio-inspired optimization techniques and a comprehensive evaluation of dose distribution, we seek to improve the precision and safety of radiation therapy, thereby advancing cancer treatment outcomes

Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty

In radiation therapy, intensity maps involving margins have long been used to counteract the effects of dose blurring arising from motion. More recently, intensity maps with increased intensity near the edge of the tumour (edge enhancements) have been studied to evaluate their ability to offset similar effects that affect tumour coverage. In this paper, we present a mathematical methodology to derive margin and edge-enhanced intensity maps that aim to provide tumour coverage while delivering minimum total dose. We show that if the tumour is at most about twice as large as the standard deviation of the blurring distribution, the optimal intensity map is a pure scaling increase of the static intensity map without any margins or edge enhancements. Otherwise, if the tumour size is roughly twice (or more) the standard deviation of motion, then margins and edge enhancements are preferred, and we present formulae to calculate the exact dimensions of these intensity maps. Furthermore, we extend our analysis to include scenarios where the parameters of the motion distribution are not known with certainty, but rather can take any value in some range. In these cases, we derive a similar threshold to determine the structure of an optimal margin intensity map.National Cancer Institute (U.S.) (grant R01-CA103904)National Cancer Institute (U.S.) (grant R01-CA118200)Natural Sciences and Engineering Research Council of Canada (NSERC)Siemens AktiengesellschaftMassachusetts Institute of Technology. Hugh Hampton Young Memorial Fund fellowshi

Robustness of target dose coverage to motion uncertainties for scanned carbon ion beam tracking therapy of moving tumors

Beam tracking with scanned carbon ion radiotherapy achieves highly conformal target dose by steering carbon pencil beams to follow moving tumors using real-time magnetic deflection and range modulation. The purpose of this study was to evaluate the robustness of target dose coverage from beam tracking in light of positional uncertainties of moving targets and beams. To accomplish this, we simulated beam tracking for moving targets in both water phantoms and a sample of lung cancer patients using a research treatment planning system. We modeled various deviations from perfect tracking that could arise due to uncertainty in organ motion and limited precision of a scanned ion beam tracking system. We also investigated the effects of interfractional changes in organ motion on target dose coverage by simulating a complete course of treatment using serial (weekly) 4DCTs from six lung cancer patients. For perfect tracking of moving targets, we found that target dose coverage was high (V¯95 was 94.8% for phantoms and 94.3% for lung cancer patients, respectively) but sensitive to changes in the phase of respiration at the start of treatment and to the respiratory period. Phase delays in tracking the moving targets led to large degradation of target dose coverage (up to 22% drop for a 15° delay). Sensitivity to technical uncertainties in beam tracking delivery was minimal for a lung cancer case. However, interfractional changes in anatomy and organ motion led to large decreases in target dose coverage (target coverage dropped approximately 8% due to anatomy and motion changes after 1 week). Our findings provide a better understand of the importance of each of these uncertainties for beam tracking with scanned carbon ion therapy and can be used to inform the design of future scanned ion beam tracking systems

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Robust Multi-Class Multi-Period Scheduling of MRI Services with Wait Time Targets

In recent years, long wait times for healthcare services have become a challenge in most healthcare delivery systems in Canada. This issue becomes even more important when there are priorities in patients' treatment which means some of the patients need emergency treatment, while others can wait longer. One example of excessively long wait times in Canada is the MRI scans. These wait times are partially due to limited capacity and increased demand, but also due to sub-optimal scheduling policies. Patients are typically prioritized by the referring physician based on their health condition, and there is a wait time target for each priority level. The difficulty of scheduling increases due to uncertainty in patients' arrivals and service times. In this thesis, we develop a multi-priority robust optimization (RO) method to schedule patients for MRI services over a multi-period finite horizon. First, we present a deterministic mixed integer programming model which considers patient priorities, MRI capacity, and wait time targets for each priority level. We then investigate robust counterparts of the model by considering uncertainty in patients' arrivals and employing the notion of the budget of uncertainty. Finally, we apply the proposed robust model to a set of numerical examples and compare the results with those of the non-robust method. Moreover, sensitivity analysis is performed over capacity, penalty cost, service level, and budget of uncertainty. Our results demonstrate that the proposed robust approach provides solutions with higher service levels for each priority, and lower patients' wait time in realistic problem instances. The analysis also provides some insights on expanding capacity and choosing the budget of uncertainty as a trade-off between performance and conservatism

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients’ clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited