Manual Planning Research Articles

Validation of Lexicographic Optimization-Based Planning for Brain Metastases Radiosurgery with Coplanar Arcs

<h3>Purpose/Objective(s)</h3> This study aimed to validate a not yet commercially available fully-automated lexicographic optimization planning for intracranial stereotactic radiosurgery (SRS) with coplanar arcs. <h3>Materials/Methods</h3> Twenty-one single-lesion SRS treatment plans (21 Gy/1 fx) delivered between November 2019 and December 2021 were retrospectively selected and automatically re-planned. An a-priori assigned priority list, a so-called Wish List (WL), was used to define the sequential multi-criterial optimization: four patient sets were necessary to achieve a robust WL. All plans were optimized with 2 coplanar 140°-arcs and calculated with the Monte Carlo algorithm (1 mm-dose grid, 0.5%-statistical uncertainty). The main criteria for planning approval was a brain volume receiving more than 12 Gy less than 10 cm³ (V_12Gy<10 cm³). A target coverage as high as possible was requested, with at least the 80% of the prescription dose covering the 99% of the planning target volume (PTV). Manual plans (MP) and automatic plans (AP) were compared in terms of dose-volume constraints and monitor units (MUs). Statistical significance was assessed performing the Wilcoxon-Mann-Whitney test with Bonferroni correction for multiple tests (alpha=0.05). Plan deliverability was verified by pre-treatment QA and gamma index analysis. <h3>Results</h3> The 21 AP re-planning took only 5 working days. The median D_98% of the gross tumor volume (GTV) and the PTV for MP and AP was 20.4 Gy [18.5-21.6] and 18.1 Gy [16.8-19.9], and 21.1 Gy [19.2-21.7] and 19.1 Gy [17.6-19.4], respectively. These increases were statistically significant (p<0.03). A significant improvement in PTV Paddick's conformity index has been registered: 0.3[0.0-0.6] for MP and 0.5[0.3-0.9] for AP with a p<0.001. The brain V_12Gy was 7.3 cm³ [4.4-12.7] and 7.6 cm³[4.2-13.3] for MP and AP, respectively (p>0.05). Other organs at risk were never significantly interested by clinically relevant doses. These AP results were obtained with a lower median number of MU (-11.6%) even if this difference was not statistically significant and plans registered a comparable gamma analysis (local 2%/2mm). <h3>Conclusion</h3> The novel autoplanning produced high-quality clinically acceptable radiosurgery plans with coplanar arcs, significantly reducing the overall planning time from about one working day for one MP to about 2 hours for one AP. While the OAR sparing was comparable between MP and AP, the target coverage was significantly increased, reducing the MU number and preserving the plan deliverability. The validation proved the capability to generate high-quality deliverable plans according to institutional-specific planning protocols.

Automated Radiation Therapy Treatment Planning Using Meta-Optimization

<h3>Purpose/Objective(s)</h3> Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP). <h3>Materials/Methods</h3> Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e., parallel Nelder-Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g., dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github (https://github.com/chh105/MetaPlanner). <h3>Results</h3> The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics. <h3>Conclusion</h3> Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.

Evaluation of a hybrid automatic planning solution for rectal cancer

BackgroundScript-based planning and knowledge-based planning are two kinds of automatic planning solutions. Hybrid automatic planning may integrate the advantages of both solutions and provide a more robust automatic planning solution in the clinic. In this study, we evaluated and compared a commercially available hybrid planning solution with manual planning and script-based planning.MethodsIn total, 51 rectal cancer patients in our institution were enrolled in this study. Each patient generated 7 plans: one clinically accepted manual plan ({plan}_{manual}), three script-based plans and three hybrid plans generated with the volumetric-modulated arc therapy technique and 3 different clinical goal settings: easy, moderate and hard ({plan}_{hybrid}^{easy}, {plan}_{hybrid}^{moderate}, {plan}_{hybrid}^{hard}, {plan}_{script}^{easy}, {plan}_{script}^{moderate} and {plan}_{script}^{hard}). Planning goals included planning target volume (PTV) Dmax, bladder Dmean and femur head Dmean. The PTV prescription was the same (50.00 Gy) for the 3 goal settings. The hard setting required a lower PTV Dmax and stricter organ at risk (OAR) dose, while the easy setting was the opposite. Plans were compared using dose metrics and plan quality metric (PQM) scores, including bladder D15 and D50, left and right femur head D25 and D40, PTV D2, D98, CI (conformity index) and HI (homogeneity index).ResultsCompared to manual planning, hybrid planning with all settings significantly reduced the OAR dose (p < 0.05, paired t-test or Wilcoxon signed rank test) for all dose-volume indices, except D25 of the left femur head. For script-based planning, {plan}_{script}^{easy} significantly increased the OAR dose for the femur head and D2 and the PTV homogeneity index (p < 0.05, paired t-test or Wilcoxon signed rank test). Meanwhile, the maximum dose of the PTV was largely increased with hard script-based planning (D2 = 56.06 ± 7.57 Gy). For all three settings, the comparison of PQM between hybrid planning and script-based planning showed significant differences, except for D25 of the left femur head and PTV D2. The total PQM showed that hybrid planning could provide a better and more robust plan quality than script-based planning.ConclusionsThe hybrid planning solution was manual-planning comparable for rectal cancer. Hybrid planning can provide a better and more robust plan quality than script-based planning.

An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem

The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based aircraft fleets in hangar bays is investigated to improve the maintenance efficiency of aircraft carrier hangar bays. First, the operational process of scheduling aeronautical maintenance tasks is systematically analyzed. Based on maintenance resource constraints and actual maintenance task requirements, a wave availability index and load balance index for the maintenance personnel are proposed for optimization. An aeronautical maintenance task scheduling model is formulated for carrier-based aircraft fleets. Second, model abstraction is performed to simulate a multi-skill resource-constrained project scheduling problem, and an improved teaching-learning-based optimization algorithm is proposed. The algorithm utilizes a serial scheduling generation scheme based on resource constraint advancement. Finally, the feasibility and effectiveness of the modeling and algorithm are verified by using simulation cases and algorithm comparisons. The improved teaching-learning-based optimization algorithm exhibits improved solution stability and optimization performance. This method provides theoretical support for deterministic aeronautical maintenance scheduling planning and reduces the burden associated with manual scheduling and planning.

Machine learning-based automated planning for hippocampal avoidance prophylactic cranial irradiation.

Design and evaluate a knowledge-based model using commercially available artificial intelligence tools for automated treatment planning to efficiently generate clinically acceptable hippocampal avoidance prophylactic cranial irradiation (HA-PCI) plans in patients with small-cell lung cancer. Data from 44 patients with different grades of head flexion (range 45°) were used as the training datasets. A Rapid Plan knowledge-based planning (KB) routine was applied for a prescription of 25Gy in 10 fractions using two volumetric modulated arc therapy (VMAT) arcs. The 9 plans used to validate the initial model were added to generate a second version of the RP model (Hippo-MARv2). Automated plans (AP) were compared with manual plans (MP) according to the dose-volume objectives of the PREMER trial. Optimization time and model quality were assessed using 10 patients who were not included in the first 44 datasets. A 55% reduction in average optimization time was observed for AP compared to MP. (15 vs 33min; p = 0.001).Statistically significant differences in favor of AP were found for D98% (22.6 vs 20.9Gy), Homogeneity Index (17.6 vs 23.0) and Hippocampus D mean (11.0 vs 11.7Gy). The AP met the proposed objectives without significant deviations, while in the case of the MP, significant deviations from the proposed target values were found in 2 cases. The KB model allows automated planning for HA-PCI. Automation of radiotherapy planning improves efficiency, safety, and quality and could facilitate access to new techniques.

O046 / #624 AUTOMATIC PATH PLANNING FOR STEREOTACTIC HIPPOCAMPAL TARGETING WITH OPTIMIZED PENETRATION IN HIPPOCAMPAL STIMULATION: TRACK 2: BRAIN STIMULATION FOR TREATMENT RESISTANT DEPRESSION, PAIN, AND PARKINSON'S DISEASE

The current gold standard workflow for stereotactic targeting of structures in the brain involves preoperative manual path planning by a neurosurgeon. This is often time-consuming and laborious especially for multiple targets.

Validation of a novel path planner for stereotactic neurosurgical interventions-A retrospective clinical study.

The gold standard workflow for targeting structures in the brain involves manual path planning. This preoperative manual path planning is very time-intensive and laborious, especially when some outcome measures such as maximum ablation and penetration depth has to be optimised. Our novel path planner generates an optimal path which maximises the hippocampus penetration and distance from critical structures using a precomputed cost map and a reward map. The average penetration ratio for 12 cases is 88.13±23.23% for a resolution of 1° and a safety margin of 1mm. Average run time for the path planner based on 1° resolution was 1.99±0.68min. Results show that the algorithm can generate safe and clinically relevant paths with a quantitative representation of the penetration depth and is faster than the average reported time for manual path planning.

A Script-Based Automatic Intensity Modulated Radiation Therapy Planning Method With Robust Optimization for Craniospinal Irradiation

This report describes a script-based automatic planning method with robust optimization for craniospinal irradiation (CSI) to reduce sensitivity to field matching errors and increase planning efficiency. The data of 10 CSI patients with planning target volume (PTV) lengths between 49.8 and 85.0 cm were retrospectively studied. Robust intensity modulated radiation therapy plans with ±5-mm longitudinal position uncertainty were generated by the automatic planning script. A simple dose prediction model and a self-adjusting method were implied in the automatic plans. The plans’ robustness against setup errors was evaluated by deliberately shifting the middle beamset ±5 mm in the superior-inferior direction. Manual and nonrobust plans were also created to evaluate the automatic robust plans’ quality, efficiency, and robustness. There were no significant differences between the manual and automatic plans in terms of homogeneity index; conformity index; D1%, D2%, and D98% of PTV; and average doses of organs at risk. However, the D99% of the PTV in the automatic plans was slightly inferior to that in the manual plans. Compared with the manual plans, the automatic plans greatly increased efficiency, with a reduction in planning time of approximately 48%. When ±5-mm superior-inferior errors were introduced, the average deviations of the maximum dose D1% and minimum dose D99% to the spinal cord were 4.9% (±1.1%) and –3.4% (±1.3%), respectively. However, the corresponding values of the nonrobust plans were 20.0% (±5.4%) and –21.2 (±6.3%), respectively. The script-based automatic CSI planning method, combining robust optimization and a dose prediction model, efficiently created a good-quality plan that was robust to setup errors.

Epidemiology-constrained Seating Plan Problem

Abstract The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could a affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.

A novel knowledge-based prediction model for estimating an initial equivalent uniform dose in semi-auto-planning for cervical cancer

BackgroundWe developed a novel concept, equivalent uniform length (EUL), to describe the relationship between the generalized equivalent uniform dose (EUD) and the geometric anatomy around a tumor target. By correlating EUL with EUD, we established two EUD–EUL knowledge-based (EEKB) prediction models for the bladder and rectum that predict initial EUD values for generating quality treatment plans.MethodsEUL metrics for the rectum and bladder were extracted and collected from the intensity-modulated radiotherapy therapy (IMRT) plans of 60 patients with cervical cancer. The two EEKB prediction models were built using linear regression to establish the relationships between EULr and EUDr (EUL and EUD of rectum) and EULb, and EUDb (EUL and EUD of bladder), respectively. The EE plans were optimized by incorporating the predicted initial EUD parameters for the rectum and bladder with the conventional pinnacle auto-planning (PAP) initial dose parameters for other organs. The efficiency of the predicted initial EUD values were then evaluated by comparing the consistency and quality of the EE plans, PAP plans (based on default PAP initial parameters), and manual plans (designed manually by different dosimetrists) for a sample of 20 patients.ResultsLinear regression analyses showed a significant correlation between EUL and EUD (R2 = 0.79 and 0.69 for EUDb and EUDr, respectively). In a sample of 20 patients, the average bladder V40 and V50 derived from the EE plans were significantly lower (V40: 30.00 ± 5.76, V50: 14.36 ± 4.00) than the V40 and V50 values derived from manual plans (V40: 36.03 ± 8.02, V50: 19.02 ± 5.42). Compared with the PAP plans, the EE plans produced significantly lower average V30 and Dmean values for the bladder (V30: 50.55 ± 6.33, Dmean: 31.48 ± 1.97 Gy).ConclusionsOur EEKB prediction models predicted reasonable initial EUD values for the rectum and bladder based on patient-specific geometric EUL values, thereby improving optimization and planning efficiency.

Development and evaluation of a three-step automatic planning technique for lung SBRT based on performance examination of advanced settings in Pinnacle's auto-planning module

The benefits of Pinnacle's auto-planning module on clinical practice have been well documented. However, little is known regarding the efficiency of its Advanced Settings and the practicality of incorporating this module into Stereotactic Body Radiation Therapy (SBRT), which is why this research was conducted. To characterize the impact of Advanced Settings on plan quality, a total of 25 previously delivered postoperative cervical cases were re-planned and evaluated. Then a three-step automatic planning technique was developed and tested on ten lung SBRT cases based on the investigation. The differences between plans with fine-tuned Advanced Settings and the default were compared using a Wilcoxon signed-rank test with a significance threshold of 5%. The same statistical analysis was implemented to examine the quality variations in manual and automatic SBRT planning. When the Tuning Balance, Dose Fall-Off Margin, and Hot-Spot Maximum Goal were set to 100%, 1 cm, and 250%, respectively, better organ-at-risk (OAR) sparing was reached, but target quality was compromised. The OAR dose reduction and target homogeneity deterioration showed a strong correlation. The three-step methodology improved high dose spillage while saving time, with statistically significant reductions of 66.7% in V105% of non-PTV and 58.1% in planning time to the human-driven strategy. Except for urgent requirements for sparing OARs or processing SBRT plans, keeping the default is appropriate for Advanced Settings. The three-step methodology automatically searches for the available solution with purposeful Advanced Settings adjustments, demonstrating its ability to produce high-quality plans in less time. For the inexperienced or under-resourced clinics, our procedure can be introduced as a robust and handy strategy in SBRT, notably for expedited quality planning.

Training and validation of a knowledge-based dose-volume histogram predictive model in the optimisation of intensity-modulated proton and volumetric modulated arc photon plans for pleural mesothelioma patients

BackgroundTo investigate the performance of a narrow-scope knowledge-based RapidPlan (RP) model for optimisation of intensity-modulated proton therapy (IMPT) and volumetric modulated arc therapy (VMAT) plans applied to patients with pleural mesothelioma. Second, estimate the potential benefit of IMPT versus VMAT for this class of patients.MethodsA cohort of 82 patients was retrospectively selected; 60 were used to "train" a dose-volume histogram predictive model; the remaining 22 provided independent validation. The performance of the RP models was benchmarked, comparing predicted versus achieved mean and near-to-maximum dose for all organs at risk (OARs) in the training set and by quantitative assessment of some dose-volume metrics in the comparison of the validation RP-based data versus the manually optimised training datasets. Treatment plans were designed for a prescription dose of 44 Gy in 22 fractions (proton doses account for a fixed relative biological effectiveness RBE = 1.1).ResultsTraining and validation RP-based plans resulted dosimetrically similar for both VMAT and IMPT groups, and the clinical planning aims were met for all structures. The IMPT plans outperformed the VMAT ones for all OARs for the contra-lateral and the mean and low dose regions for the ipsilateral OARs. Concerning the prediction performance of the RP models, the linear regression for the near-to-maximum dose resulted in Dachieved = 1.03Dpredicted + 0.58 and Dachieved = 1.02Dpredicted + 1.46 for VMAT and IMPT, respectively. For the mean dose it resulted: Dachieved = 0.99Dpredicted + 0.34 and Dachieved = 1.05Dpredicted + 0.27 respectively. In both cases, the linear correlation between prediction and achievement is granted with an angular coefficient deviating from unity for less than 5%. Concerning the dosimetric comparison between manual plans in the training cohort and RP-based plans in the validation cohort, no clinical differences were observed for the target volumes in both the VMAT and IMPT groups. Similar consistency was observed for the dose-volume metrics analysed for the OAR. This proves the possibility of achieving the same quality of plans with manual procedures (the training set) or with automated RP-based methods (the validation set).ConclusionTwo models were trained and validated for VMAT and IMPT plans for pleural mesothelioma. The RP model performance resulted satisfactory as measured by the agreement between predicted and achieved (after full optimisation) dose-volume metrics. The IMPT plans outperformed the VMAT plans for all the OARs (with different intensities for contra- or ipsilateral structures). RP-based planning enabled the automation of part of the optimisation and the harmonisation of the dose-volume results between training and validation. The IMPT data showed a systematic significant dosimetric advantage over VMAT. In general, using an RP-based approach can simplify the optimisation workflow in these complex treatment indications without impacting the quality of plans.

A study on deep reinforcement learning-based crane scheduling model for uncertainty tasks

Abstract Aiming at the crane scheduling problem for uncertainty tasks in multi-crane scheduling situation, this article proposes a deep reinforcement learning-based crane scheduling modeling method that is not dependent on mathematical planning and has certain generality. First, the crane scheduling process is integrated into deep reinforcement learning framework in which the orbit space of the crane and the transportation task is environmental information and crane is the intelligent agent. Second, the interactive mode between reinforcement learning algorithm and environment is adjusted to adapt to the combined learning of multi-crane scheduling model. Last, the crane scheduling model for uncertainty tasks is constructed by optimizing reward discount factor, learning rate, and reward function intensive mode. Testing of the model is carried out based on practical crane scheduling in one steelmaking workshop. Scheduling proposal is generated and all crane tasks are completed within the planned time, which verifies the feasibility of this model. Results show that compared with manual scheduling plan, the scheduling proposal based on the new model reduces total task completion time by 11.52%, time of collision of crane routes decreases by 57.14%, and negative crane transportation distance shortens by 55.26%. The high efficiency of the scheduling model is therefore verified.

Multi-product multi-region supply chain optimisation for seasonal crops

There is a large demand for seasonal crops year-round within Australia, even when they are considered out of season. The demand is satisfied by continually moving production throughout the year to climates where the crops are in season. Management of the supply chain for a major national grower is challenging for several reasons. Due to the large number of planting decisions, it is standard practice for a team of production planners to create the annual production plan. For fresh food production, the supply chain does not contain intermediate storage requiring that the production plan is carefully timed not to waste resources with overproduction. In this paper, we develop a supply chain model that simultaneously manages the production of multiple crops across many growing regions. Production is set to satisfy the demand of multiple end-products while considering the packing plants' throughput capacity, each growing region's harvest capacity, and farm capacity. A time delay may be applied when moving between stages of the supply chain due to the geographic scale being modelled. A deterministic Mixed Integer Program is used to find the optimal planting plan, which minimises the deviation from demand for all products year-round at a minimal cost. Due to the excessive runtime for solving the model, a heuristic solution method is introduced. Numerical experiments demonstrate the advantage of the proposed model over the current manual planning process, which can solve the problem faster and with less deviation across the planning horizon.

Benchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer.

In daily plan adaptation the radiotherapy treatment plan is adjusted just prior to delivery. A simple approach is taking the planning objectives of the reference plan and directly applying these in re-optimization. Here we present a tested method to verify whether daily adaptation without tweaking of the objectives can maintain the plan quality throughout treatment. For fifteen rectal cancer patients, automated treatment planning was used to generate plans mimicking manual reference plans on the planning scans. For 74 fraction scans (4-5 per patient) an automated plan and a daily adapted plan were generated, where the latter re-optimizes the reference plan objectives without any tweaking. To evaluate the robustness of the daily adaptation, the adapted plans were compared to the autoplanning plans. Median differences between the autoplanning plans on the planning scans and the reference plans were between -1 and 0.2Gy. The largest interquartile range (1Gy) was seen for the Lumbar Skin D2%. For the daily scans the PTV D2% and D98% differences between autoplanning and adapted plans were within 0.7Gy, with mean differences within 0.3Gy. Positive differences indicate higher values were obtained using autoplanning. For the Bowelarea+Bladder and the Lumbar Skin the D2% and Dmean differences were all within 2.6Gy, with mean differences between -0.9 and 0.1Gy. Automated treatment planning can be used to benchmark daily adaptation techniques. The investigated adaptation workflow can robustly perform high quality adaptations without daily adjusting of the patient-specific planning objectives for rectal cancer radiotherapy.

DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

Fingerprinting-based indoor localization is a cost-effective approach to provide coarse-grained indoor location information for pedestrian mass-market applications without the requirement of installing additional positioning infrastructure. While most solutions aim at pinpointing the exact location of a user, estimating a zone/area is a promising approach to achieve a more reliable prediction. Area localization predominantly utilizes a predetermined building model segmentation to obtain zone/area labels for collected fingerprints. We propose a novel approach to multifloor indoor area localization by directly predicting polygon zones that contain the position of the user. Our model learns to construct the zones from the wall segments and thus predicted areas have a high conformity to the underlying building model (semantic expressiveness). On a self-collected as well as on a public fingerprinting data set, we compare our model with two reference approaches. We demonstrate that the utilized surface areas of the polygons are on average up to 50% smaller than those of the reference models and provide a high semantic expressiveness without requiring manual floor plan segmentation.

Cognitive, perceptual, and motor profiles of school-aged children with developmental coordination disorder.

Developmental coordination disorder (DCD) is a heterogeneous condition. Besides motor impairments, children with DCD often exhibit poor visual perceptual skills and executive functions. This study aimed to characterize the motor, perceptual, and cognitive profiles of children with DCD at the group level and in terms of subtypes. A total of 50 children with DCD and 31 typically developing (TD) peers (7–11 years old) underwent a comprehensive neuropsychological (15 tests) and motor (three subscales of the Movement Assessment Battery for Children-2) assessment. The percentage of children with DCD showing impairments in each measurement was first described. Hierarchical agglomerative and K-means iterative partitioning clustering analyses were then performed to distinguish the subtypes present among the complete sample of children (DCD and TD) in a data-driven way. Moderate to large percentages of children with DCD showed impaired executive functions (92%) and praxis (meaningless gestures and postures, 68%), as well as attentional (52%), visual perceptual (46%), and visuomotor (36%) skills. Clustering analyses identified five subtypes, four of them mainly consisting of children with DCD and one of TD children. These subtypes were characterized by: (i) generalized impairments (8 children with DCD), (ii) impaired manual dexterity, poor balance (static/dynamic), planning, and alertness (15 DCD and 1 TD child), (iii) impaired manual dexterity, cognitive inhibition, and poor visual perception (11 children with DCD), (iv) impaired manual dexterity and cognitive inhibition (15 DCD and 5 TD children), and (v) no impairment (25 TD and 1 child with DCD). Besides subtle differences, the motor and praxis measures did not enable to discriminate between the four subtypes of children with DCD. The subtypes were, however, characterized by distinct perceptual or cognitive impairments. These results highlight the importance of assessing exhaustively the perceptual and cognitive skills of children with DCD.

Using a route planner to optimize door-to-door visits for a pediatric home-hospitalization program: Feasibility study.

BackgroundHospital Sant Joan de Déu (Barcelona) initiated a pediatric acute home-hospitalization program. Due to high patient turnover and the health staff's lack of planning training, daily scheduling was a time-consuming task. Home-hospitalization planning is a vehicle routing problem that can be solved with a technological solution. It was therefore decided to evaluate the efficacy and necessity of the SmartMonkey.io planner.ObjectivesTo compare traditional manual route planning with a route optimizer, and to evaluate the technical feasibility of the implementation of a route planner into a homecare program.MethodsEight participants (experienced homecare staff and inexperienced hospital staff) were included. Personal interviews were performed to assess their eagerness to try a technological solution to the planning problem. Objective benefits including reduced travel time (time planning, distance traveled, and time traveled) were evaluated. Paired t-test, t-test, and Pearson's correlation were used to compare manual and route planner scheduling. Participants then answered a questionnaire to assess planning difficulty and the acceptance of the route planner.ResultsHomecare staff were initially reluctant to use the technology. Significant differences (P < 0.0001) in three variables were found between manual planning and the route planner. A moderate correlation between time planning and plan difficulty (r = 0.59, P < 0.0001) was found with manual planning but not with the route planner. All route planner schedules saved time and distance. No significant differences were found between expertise and planning method. It was noted that it was easy to create plans with the route planner, while difficulty with manual planning increased as more locations were added. All participants evaluated the route planning tool favorably.ConclusionsRoute-planning technology saved planning time and generated better plans than manual planning. The route planner's learning curve was fast and results were obtained in the same amount of time regardless of difficulty and expertise. SmartMonkey.io also has the potential to reduce internal and environmental costs and increase staff productivity.

Validation of RapidPlan Knowledge-Based Model for Volumetric-Modulated Arc Therapy in Prostate Cancer.

This study aims to investigate the viability of using RapidPlan (RP) knowledge-based (KB) treatment plans to initiate the new prostate volumetric-modulated arc therapy (VMAT) plans. The planning data for 120 prostate VMAT treatment plans were entered into the RP system's database. The database of previous VMAT plans was divided into four model groups for training in the RP system. The models were based on the numbers of 20, 60, and 120 prostate VMAT plans. The model of 120 plans used automated priority and manual priority for the optimization process. The models of 20 and 60 plans used only manual priority for optimization. Each model was validated on 15 cases of new prostate cancer patients by comparing RP model plans against manual clinical plans optimized according to the clinical dose constraints. The RP models can estimate the dose comparable target volume to the manual clinical plan, which evaluated values of Dmax, D95%, D98%, HI, and CI and showed comparable results. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP models exhibited a comparable or better dose than the manual clinical plan, except for the RP models using the automated priority for the optimization process, which cannot control the rectum dose below the dose constraints. The Varian RP KB planning can produce comparable doses or better doses with the clinical manual in a single optimization, although the RP model uses a minimum requirement of the planning number for the model training. The RP models can enhance the efficacy and quality of plans, which depend on the number of VMAT plans used in RP model training for prostate cancer.

Цифровой карьер: использование горно-геологической информационной системы при планировании горных работ

Creation of mine development plans for mining operations in the ferrous and non-ferrous metals industry is a timeconsuming and creative process that requires high levels of specialist expertise, especially in manual planning, where it is quite difficult to assess the impact of decisions made hic et nunc on the future development of the company. Application of mining and geological systems in planning makes it possible to simulate and assess many different options in mine development within a short period time and with sufficiently high reliability. These systems allow planning specialists to make more informed and correct decisions without the fear that these decisions may lead to deterioration of open pits or mines. The article describes the course of project implementation to transform the business processes of mine planning using mining and geological information system by Dassault Systemes at the METALLOINVEST Group of Companies as exemplified by the Mikhailovsky GOK Lebedinsky GOK mines.