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Constructive simulations are the applications used by the military for the training of their commanders in planning and analysis of various threats and Courses of Action. In the ‘analysis wargames’, there are need to automate many of the tasks of the commander which are carried out by subunit commanders on the ground. Deployment of defence units is one of such important decision making by commander. Deployments of units (and sub units) is dependent on multiple factors which needs to be satisfied/optimised for meeting the given objective of the unit. In this paper we have attempted to solve the multi criterion decision problem of optimal deployment of defence units in mountainous terrain using Particle Swarm Optimization(PSO) and Adaptive Particle Swarm Optimization(APSO). The algorithm has been tested with varied number of decision parameters and their weights using digital elevation and vector data of the terrain features. The auto deployment outcomes are found satisfactory. Our solution approach has potential in automated planning in constructive simulations. [ABSTRACT FROM AUTHOR]

Background and Purpose: The lack of dedicated tools in commercial planning systems currently restricts efficient review and planning for re-irradiation. The aim of this study was to develop an automated re-irradiation planning framework based on cumulative doses. Materials and Methods: We performed a retrospective study of 14 patients who received spine SBRT re-irradiation near a previously irradiated treatment site. A fully-automated workflow, DART (Dose Accumulation-based Re-irradiation Tool), was implemented within Eclipse by leveraging a combination of a dose accumulation script and a proprietary automated optimization algorithm. First, we converted the prior treatment dose into equivalent dose in 2 Gy fractions (EQD2) and mapped it to the current anatomy, utilizing deformable image registration. Subsequently, the intersection of EQD2 isodose lines with relevant organs at risk defines a series of optimization structures. During plan optimization, the residual allowable dose at a specified tissue tolerance was treated as a hard constraint. Results: All DART plans met institutional physical and cumulative constraints and passed plan checks by qualified medical physicists. DART demonstrated significant improvements in target coverage over clinical plans, with an average increase in PTV D99% and V100% of 2.3 Gy [range −0.3–7.7 Gy] and 3.4 % [range −0.4 %−7.6 %] (p 105 %) outside the PTV was reduced by up to 7 cm3. The homogeneity index for DART plans was improved by 19 % (p

Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal–Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs. [ABSTRACT FROM AUTHOR]

Personalized sequencing and recommendation of pedagogical actions in virtual learning environments are relevant aspects in promoting an effective learning process with computer-aided support. Hence, this work investigates the use of automated planning to sequence these actions according to student profiles. Actions are modeled to correspond to the cognitive process described by Bloom's taxonomy, and the student profile is set using the revised approaches to studying inventory. Both models share theoretical foundations linked to the cognitive process, and the mapping of these two theories is one of the contributions merged into this study. In planning, through use of a genetic algorithm, and the problem formulation as an optimization problem, one can correctly manage the search for good solutions, as demonstrated in this work. Through use of digital Bloom's taxonomy, one arrives at a recommend set of actions. Experiments were performed using 41 students. The results were promising and demonstrate the viability of the proposal.

This study evaluates the enhancement of planning automation in radiation oncology through the integration of physician-specific preferences for organ at risk (OAR) prioritization into an automated planning framework. By combining physician-defined OAR dose goals with multi-criterial driven automated treatment planning, the objective is to refine plan quality and physician satisfaction, ensuring patient-specific considerations are forefront. The study introduces an advanced automation solution that incorporates a prescription device (HRO) to input physician prescription and OAR preferences, connected to an existing planning automation engine utilizing a treatment planning software through API integration. Focusing on head and neck cancer plans, the method involves analyzing the automated planning system's efficacy in optimizing dose distribution in line with physician-set OAR priorities. The study evaluated plans in a network of 40 treatment centers, processing over 20,000 automated treatment plans annually. The system's performance was evaluated based on compliance to physician-defined OAR dose goals, planning efficiency, and the percentage of treatment plans that are approved for treatment without manual user intervention. The analysis revealed that the system successfully influenced the treatment planning system to adjust dose distribution according to physician-defined OAR priorities, with physicians able to layer organs at risk to define prioritization effectively. The system's integration with the prescription device demonstrated a notable improvement in plan quality with 62% of plans showing a reduced Parotid mean dose without compromising adjacent structures. Target coverage improved on 29% of cases while maximum dose decreased on 36% of cases while maintaining greater Parotid sparing. The approach provided physicians with the ability to influence the outcomes of complex dose optimization within a TPS without intervention, reflecting the complex interplay of clinical objectives and individual patient anatomy. The integration of physician preferences into automated planning systems represents a significant advancement in personalized radiation therapy planning. By enabling physicians to dictate OAR priorities directly, the study demonstrates improved alignment of automated plans with individual patient anatomy and clinical goals. This hybrid approach not only enhances plan quality and efficiency but also fosters greater physician satisfaction and confidence in automated systems. The findings advocate for greater provider wellness and more sustainable operating models for physicians through more patient centric automated solutions. [ABSTRACT FROM AUTHOR]

Purpose. Patient-tailored intracavitary/interstitial (IC/IS) brachytherapy (BT) applicators may increase dose conformity in cervical cancer patients. Current configuration planning methods in these custom applicators rely on manual specification or a small set of (straight) needles. This work introduces and validates a two-stage approach for establishing channel configurations in the 3D printed patient-tailored ARCHITECT applicator. Methods. For each patient, the patient-tailored applicator shape was based on the first BT application with a commercial applicator and integrated connectors to a commercial (Geneva) intrauterine tube and two lunar ring channels. First, a large candidate set was generated of channels that steer the needle to desired poses in the target region and are contained in the applicator. The channels' centrelines were represented by Bézier curves. Channels running between straight target segments and entry points were optimised and refined to ensure (dynamic) feasibility. Second, channel configurations were selected using geometric coverage optimisation. This workflow was applied to establish patient-tailored geometries for twenty-two patients previously treated using the Venezia applicator. Treatment plans were automatically generated using the in-house developed algorithm BiCycle. Plans for the clinically used configuration,TPclin, and patient-tailored configuration,TParch, were compared. Results. Channel configurations could be generated in clinically feasible time (median: 2651 s, range 1826-3812 s). AllTParchandTPclinplans were acceptable, but planning aims were more frequently attained with patient-tailored configurations (115/132 versus 100/132 instances). Median CTV IR D98and bladderD2cm3doses significantly improved (p<0.001 andp<0.01 respectively) inTParchplans in comparison withTPclinplans, and in approximately half of the patients dosimetric indices improved. Conclusion. Automated patient-tailored BT channel configuration planning for 3D printed applicators is clinically feasible. A treatment planning study showed that all plans met planning limits for the patient-tailored configurations, and in selected cases improved the plan quality in comparison with commercial applicator configurations., (Creative Commons Attribution license.)

Background/Objectives : To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. Methods : In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its input a planning CT with organs-at-risk (OARs) and planning target volume (PTV) contours, the targeted linac machine, and the prescription dose. The primary components are (i) dose prediction by a single deep learning model for both localizations and (ii) a direct aperture VMAT plan optimization that seeks to mimic the predicted dose. The deep learning model was trained on 238 cases, and a held-out set of 86 cases was used for model validation. An end-to-end clinical evaluation study was performed on another 40 cases (20 prostate-only, 20 whole-pelvic). First, a quantitative evaluation was performed based on dose-volume histogram (DVH) points and plan parameter metrics. Then, the plan deliverability was assessed via portal dosimetry using the global gamma index. Additionally, the reference clinical manual plans were compared with the automated plans in terms of monitor unit (MU) numbers and modulation complexity scores (MCSv). Results : The automated plans provided adequate treatment plans (or minor deviations) with respect to the dose constraints, and the quality of the plans was similar to the manual plans for both localizations. Moreover, the automated plans showed successful deliverability and passed the portal dose verification. Despite higher median total MUs, no statistically significant correlation was observed between any of the gamma criteria tested and the number of MUs or MCSv. Conclusions : This study shows the feasibility of a deep learning-based fully automated treatment planning pipeline that generates high-quality plans that are competitive with manually made plans and are clinically approved in terms of dosimetry and machine deliverability.