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.

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%.

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 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.

Uncertainty incorporated beam angle optimization for IMPT treatment planning.

PURPOSE: Beam angle optimization (BAO) by far remains an important and challenging problem in external beam radiation therapy treatment planning. Conventional BAO algorithms discussed in previous studies all focused on photon-based therapies. Impact of BAO on proton therapy is important while proton therapy increasingly receives great interests. This study focuses on potential benefits of BAO on intensity-modulated proton therapy (IMPT) that recently began available to clinical cancer treatment. METHODS: The authors have developed a novel uncertainty incorporated BAO algorithm for IMPT treatment planning in that IMPT plan quality is highly sensitive to uncertainties such as proton range and setup errors. A linear programming was used to optimize robust intensity maps to scenario-based uncertainties for an incident beam angle configuration. Unlike conventional intensity-modulated radiation therapy with photons (IMXT), the search space for IMPT treatment beam angles may be relatively small but optimizing an IMPT plan may require higher computational costs due to larger data size. Therefore, a deterministic local neighborhood search algorithm that only needs a very limited number of plan objective evaluations was used to optimize beam angles in IMPT treatment planning. RESULTS: Three prostate cancer cases and two skull base chordoma cases were studied to demonstrate the dosimetric advantages and robustness of optimized beam angles from the proposed BAO algorithm. Two- to four-beam plans were optimized for prostate cases, and two- and three-beam plans were optimized for skull base cases. By comparing plans with conventional two parallel-opposed angles, all plans with optimized angles consistently improved sparing at organs at risks, i.e., rectum and femoral heads for prostate, brainstem for skull base, in either nominal dose distribution or uncertainty-based dose distributions. The efficiency of the BAO algorithm was demonstrated by comparing it with alternative methods including simulated annealing and genetic algorithm. The numbers of IMPT plan objective evaluations required were reduced by up to a factor of 5 while the same optimal angle plans were converged in selected comparisons. CONCLUSIONS: Uncertainty incorporated BAO may introduce pronounced improvement of IMPT plan quality including dosimetric benefits and robustness over uncertainties, based on the five clinical studies in this paper. In addition, local search algorithms may be more efficient in finding optimal beam angles than global optimization approaches for IMPT BAO.