Manual Planning Research Articles

Evaluation of automatic VMAT plans in locally advanced nasopharyngeal carcinoma.

This study aimed to evaluate the quality of locally advanced nasopharyngeal carcinoma (NPC) radiotherapy plans generated by the automated planning module of acommercial treatment planning system (TPS). Data of 30patients with locally advanced NPC were retrospectively investigated. For each patient, volumetric modulated arc therapy (VMAT) plans with double arcs were generated manually by experienced physicists and automatically in the Pinnacle3 Auto-Planning module (Philips Medical Systems, Fitchburg, WI, USA). The anatomic distance between the second clinical target volume (CTV2) and the pons of the brainstem, and the Tcategory of disease were factored into the evaluation. Dosimetric verification was evaluated in terms of gamma pass rate. Target coverage, sparing of organs at risk (OARs), and monitor units were evaluated and compared between the manual and automatic VMAT plans. Not all treatment plans fully met the dose objectives for planning target volumes (PTVs) and OARs, particularly in T4 patients. Overall, automatic VMAT provides acomparable or superior plan quality to manual VMAT in most cases. In stratified analysis, plan quality is mainly independent on Tcategory but is also affected by anatomic distance. If the anatomic distance is less than 5 mm, the automatic VMAT plan quality is equal or even inferior to manual VMAT performed by experienced physicists. Conversely, if the anatomic distance is greater than 5 mm, the automatic VMAT plan quality is superior to manual VMAT. Gamma pass rates for quality assurance are similar between manual and automatic VMAT plans for the former case, but significantly higher in automatic VMAT for the latter. The selection of manual versus automatic VMAT planning in locally advanced NPC should be made individually based on the anatomic distance, rather than blindly and habitually, since automatic VMAT is not good enough to completely replace manual VMAT.

A survey of welding robot intelligent path optimization

Abstract Welding robots are widely used for most welding works in manufacturing industries due to their flexible, efficient and accurate operation. At the same time, it can improve enterprise competitiveness, productivity, product quality, and reduce producing cost. For industrial welding robot task planning, manual planning is still widely used, and it is time-consuming when there are large-scale welding joints or welding seams. To meet this challenge, automatic and efficient path optimization technologies have been studied to realize effective robot path planning. For welding robot path optimization, path optimization problem description is presented first. Then, intelligent optimization algorithms for welding robot path optimization are introduced. Furthermore, obstacle avoidance strategies are studied. Besides, welding robot multi-objective path optimization is given based on the above optimization problem description and optimization algorithm analysis. Optimization results show the effectiveness of intelligent path optimization strategies. At last, the discussions on welding robot path optimization application and future research are analyzed briefly.

Personalized setting of plan parameters using feasibility dose volume histogram for auto-planning in Pinnacle system.

PurposeThe personalized setting of plan parameters in the Auto‐Planning module of the Pinnacle treatment planning system (TPS) using the PlanIQ feasibility tool was evaluated for lung cancer conventional fractionated radiotherapy (CFRT).Materials and methodWe reviewed the records of ten patients with lung cancer who were treated with volumetric modulated arc therapy (VMAT). Three plans were designed for each patient: the clinically accepted manual plan (MP) and two automatic plans including one generated using the generic plan parameters in technique script (AP1) and the other generated using personalized plan parameters derived based on feasibility dose volume histogram (FDVH) in PlanIQ (AP2). The plans were assessed according to the dosimetric parameters, monitor units, and planning time. A plan quality metric (PQM) was defined according to the clinical requirements for plan assessment.ResultsAP2 achieved better lung sparing than AP1 and MP. The PQM value of AP2 (52.5 ± 14.3) was higher than those of AP1 (49.2 ± 16.2) and MP (44.8 ± 16.9) with P < 0.05. The monitor units of AP2 (585.9 ± 142.9 MU) was higher than that of AP1 (511.1 ± 136.5 MU) and lower than that of MP (632.8 ± 143.8 MU) with p < 0.05. The planning time of AP2 (33.2 ± 4.8 min) was slightly higher than that of AP1 (28.2 ± 4.0 min) and substantially lower than that of MP (72.9 ± 28.5 min) with P < 0.05.ConclusionsThe Auto‐Planning module of the Pinnacle system using personalized plan parameters suggested by the PlanIQ Feasibility tool provides superior quality for lung cancer plans, especially in terms of lung sparing. The time consumption of Auto‐Planning was slightly higher with the personalized parameters compared to that with the generic parameters, but significantly lower than that for the manual plan.

Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer

PurposeThe first clinical genetic autoplanning algorithm (Genetic Planning Solution, GPS) was validated in ten radiotherapy centres for prostate cancer VMAT by comparison with manual planning (Manual). MethodsAlthough there were large differences among centres in planning protocol, GPS was tuned with the data of a single centre and then applied everywhere without any centre-specific fine-tuning. For each centre, ten Manual plans were compared with autoGPS plans, considering dosimetric plan parameters and the Clinical Blind Score (CBS) resulting from blind clinician plan comparisons. AutoGPS plans were used as is, i.e. there was no patient-specific fine-tuning. ResultsFor nine centres, all ten plans were clinically acceptable. In the remaining centre, only one plan was acceptable. For the 91% acceptable plans, differences between Manual and AutoGPS in target coverage were negligible. OAR doses were significantly lower in AutoGPS plans (p < 0.05); rectum D15% and Dmean were reduced by 8.1% and 17.9%, bladder D25% and Dmean by 5.9% and 10.3%. According to clinicians, 69% of the acceptable AutoGPS plans were superior to the corresponding Manual plan. In case of preferred Manual plans (31%), perceived advantages compared to autoGPS were minor. QA measurements demonstrated that autoGPS plans were deliverable. A quick configuration adjustment in the centre with unacceptable plans rendered 100% of plans acceptable. ConclusionA novel, clinically applied genetic autoplanning algorithm was validated in 10 centres for in total 100 prostate cancer patients. High quality plans could be generated at different centres without centre-specific algorithm tuning.

Conventional vs machine learning–based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial

PurposeThe purpose of this study was to evaluate the noninferiority of Day 30 dosimetry between a machine learning–based treatment planning system for prostate low-dose-rate (LDR) brachytherapy and the conventional, manual planning technique. As a secondary objective, the impact of planning technique on clinical workflow efficiency was also evaluated. Materials and Methods41 consecutive patients who underwent I-125 LDR monotherapy for low- and intermediate-risk prostate cancer were accrued into this single-institution study between 2017 and 2018. Patients were 1:1 randomized to receive treatment planning using a machine learning–based prostate implant planning algorithm (PIPA system) or conventional, manual technique. Treatment plan modifications by the radiation oncologist were evaluated by computing the Dice coefficient of the prostate V150% isodose volume between either the PIPA—or conventional—and final approved plans. Additional evaluations between groups evaluated the total planning time and dosimetric outcomes at preimplant and Day 30. Results21 and 20 patients were treated using the PIPA and conventional techniques, respectively. No significant differences were observed in preimplant or Day 30 prostate D90%, V100%, rectum V100, or rectum D1cc between PIPA and conventional techniques. Although the PIPA group had a larger proportion of patients with plans requiring no modifications (Dice = 1.00), there was no significant difference between the magnitude of modifications between each arm. There was a large significant advantage in mean planning time for the PIPA arm (2.38 ± 0.96 min) compared with the conventional (43.13 ± 58.70 min) technique (p >> 0.05). ConclusionsA machine learning–based planning workflow for prostate LDR brachytherapy has the potential to offer significant time savings and operational efficiencies, while producing noninferior postoperative dosimetry to that of expert, conventional treatment planners.

Automated planning through robust templates and multicriterial optimization for lung VMAT SBRT of lung lesions

PurposeTo develop and validate a robust template for VMAT SBRT of lung lesions, using the multicriterial optimization (MCO) of a commercial treatment planning system.MethodsThe template was established and refined on 10 lung SBRT patients planned for 55 Gy/5 fr. To improve gradient and conformity a ring structure around the planning target volume (PTV) was set in the list of objectives. Ideal fluence optimization was conducted giving priority to organs at risk (OARs) and using the MCO, which further pushes OARs doses. Segmentation was conducted giving priority to PTV coverage. Two different templates were produced with different degrees of modulation, by setting the Fluence Smoothing parameter to Medium (MFS) and High (HFS).Each template was applied on 20 further patients. Automatic and manual plans were compared in terms of dosimetric parameters, delivery time, and complexity. Statistical significance of differences was evaluated using paired two‐sided Wilcoxon signed‐rank test.ResultsNo statistically significant differences in PTV coverage and maximum dose were observed, while an improvement was observed in gradient and conformity. A general improvement in dose to OARs was seen, which resulted to be significant for chest wall V30 Gy, total lung V20 Gy, and spinal cord D0.1 cc. MFS plans are characterized by a higher modulation and longer delivery time than manual plans. HFS plans have a modulation and a delivery time comparable to manual plans, but still present an advantage in terms of gradient.ConclusionThe automation of the planning process for lung SBRT using robust templates and MCO was demonstrated to be feasible and more efficient.

BIM-based immersive Virtual Reality for construction workspace planning: A safety-oriented approach

Effective workspace planning is known to play a crucial role in site planning and construction activity scheduling. This research seeks to enhance the usual manual workspace planning process by simulating a construction activity using both immersive Virtual Reality and Building Information Modeling technologies. Workspace planning is improved by eliciting experienced workers' knowledge and integrating it with construction managers' knowledge. The workers' knowledge is elicited both directly, by asking them for feedback during the activity simulation, and indirectly, by collecting and analysing position tracking data. The application of this approach in a real case study demonstrates its beneficial impact in terms of the sharing of planning and safety-related information between project partners and its formal representation in a Health and Safety Plan.

Automated Hypofractionated IMRT treatment planning for early-stage breast Cancer

BackgroundHypofractionated whole-breast irradiation is a standard adjuvant therapy for early-stage breast cancer. This study evaluates the plan quality and efficacy of an in-house-developed automated radiotherapy treatment planning algorithm for hypofractionated whole-breast radiotherapy.MethodsA cohort of 99 node-negative left-sided breast cancer patients completed hypofractionated whole-breast irradiation with six-field IMRT for 42.56 Gy in 16 daily fractions from year 2016 to 2018 at a tertiary center were re-planned with an in-house-developed algorithm. The automated plan-generating C#-based program is developed in a Varian ESAPI research mode. The dose-volume histogram (DVH) and other dosimetric parameters of the automated and manual plans were directly compared.ResultsThe average time for generating an autoplan was 5 to 6 min, while the manual planning time ranged from 1 to 1.5 h. There was only a small difference in both the gantry angles and the collimator angles between the autoplans and the manual plans (ranging from 2.2 to 5.3 degrees). Autoplans and manual plans performed similarly well in hotspot volume and PTV coverage, with the autoplans performing slightly better in the ipsilateral-lung-sparing dose parameters but were inferior in contralateral-breast-sparing. The autoplan dosimetric quality did not vary with different breast sizes, but for manual plans, there was worse ipsilateral-lung-sparing (V4Gy) in larger or medium-sized breasts than in smaller breasts. Autoplans were generally superior than manual plans in CI (1.24 ± 0.06 vs. 1.30 ± 0.09, p < 0.01) and MU (1010 ± 46 vs. 1205 ± 187, p < 0.01).ConclusionsOur study presents a well-designed standardized fully automated planning algorithm for optimized whole-breast radiotherapy treatment plan generation. A large cohort of 99 patients were re-planned and retrospectively analyzed. The automated plans demonstrated similar or even better dosimetric quality and efficacy in comparison with the manual plans. Our result suggested that the autoplanning algorithm has great clinical applicability potential.

A Pareto-based beam orientation optimization method for spot scanning intensity-modulated proton therapy.

To provide a proof of principle of a Pareto-based method to automatically generate optimal intensity-modulated proton therapy (IMPT) plans for various noncoplanar beam orientations. A novel multicriteria beam orientation optimization (MCBOO) method was developed to generate Pareto database of optimal plans. The MCBOO method automatically explores the beam orientations and the scalarization parameters of the IMPT plans simultaneously. The MCBOO method is based on multicriteria bilevel optimization (i.e., hierarchical optimization with two nested levels, named the upper and lower level optimization). In MCBOO, the upper level optimization explores the noncoplanar beam orientation space, while the lower level explores the scalarization parameters for a given beam orientation. Differential evolution method was used in both levels, and the Pareto optimal plans were aggregated from the bilevel optimizations to construct the Pareto database. The MCBOO method was implemented on a multinode multi-GPU cluster, and it was tested on three brain tumor patient cases. The Pareto database of the three patients was generated for a set of DVH-based objectives. A statistical analysis was performed between a selected set of MCBOO plans and the manual plan (plan with manually selected beam orientation based on the clinical experience and optimized with the same single plan iterative optimizer used in the MCBOO). The selected set of MCBOO plans consisted of plans that matched the performance of the manual plan [i.e., MCBOO plans that have the same target coverage (within 2%) as the manual plan or better and achieved the same dose (within 2%) or lower to all of the organs at risks (OARs) but one OAR]. Additionally, a dosimetric comparison between of one of the selected MCBOO plans vs the manual plan was conducted. The multicriteria beam orientation optimization algorithm automatically generated Pareto plans for the three noncoplanar brain tumor cases. The MCBOO plans provided an alternative objective trade-offs to the manual plan. The selected MCBOO plans showed a reduction in dose to multiple organs at risk vs the manual plan with a maximum value which ranged between 10.8 and 12.9Gy for the three patients. The trade-off of the OAR dose reduction resulted in higher dose to no more than one OAR for each of the selected MCBOO plans vs the manual plan. The maximum dose increase in the MCBOO plans over the manual plan ranged from 7.8 to 11.8Gy. A novel multicriteria beam orientation optimization method was developed and tested on three IMPT patient cases. The method automatically generates Pareto plans database by exploring the noncoplanar beam orientations. The method was able to identify beam orientations with Pareto optimal plans that are comparable to the manually created plans with varying objective trade-offs.

Cut Order Planning Optimisation in the Apparel Industry

Nowadays, apparel businesses have to cut down on their costs in order to ensure the continuity of their activities and to expand them. Fabric costs cover about 50-60% of production costs. In this study, for the minimisation of fabric costs, the cutting department in enterprises was investigated and the cut order plans developed by mixed integer nonlinear programming. Examples were taken from the enterprises that were implemented. A mathematical model was then developed to be used in mixed integer nonlinear programming for a manual cut order plan, and a program code was created in LINGO optimisation software. With the program code developed, optimum results that cannot be manually calculated by an operator is obtained. The amounts of fabric to be used as a result of the cut order plan were applied to the samples taken from the enterprises, and the manual solution and model developed were compared. As a result of the comparisons made, thanks to the model, fabric developed, usage is reduced and fabric cost minimisation ensured.

A preliminary study on a multiresolution-level inverse planning approach for Gamma Knife radiosurgery.

With many plan variables to determine, manual forward planning for Gamma Knife (GK) radiosurgery is very challenging. Inverse planning eases GK planning by determining the variables via solving an optimization problem. However, due to the vast search space, most inverse planning algorithms, including the one provided in Leksell GammaPlan (LGP) treatment planning system, have to predetermine the isocenter locations using some geometric methods and then optimize the shot shapes and durations at these preselected isocenters. This sequential planning scheme does not necessarily lead to optimal isocenter locations and hence globally optimal plans. In this study, we proposed a multiresolution-level (MRL) inverse planning approach, attempting to approach this large-scale GK optimization problem via an iterative method. In our MRL approach, several rounds of optimizations were performed with a progressively increased resolution used for isocenter candidates. At each round, an optimization problem was solved to optimize the beam-on time for each collimator and sector at each isocenter candidate. The isocenters that obtained nonzero beam-on times at the previous round and their neighbors on a finer resolution were used as new isocenter candidates for the next round of optimization. After plan optimization, shot sequencing was performed to group the optimized sectors to deliverable composite shots. We have tested our MRL approach on six GK cases previously treated in our institution. For the five cases that have a single target, with similar target coverage obtained, our MRL inverse planning approach achieved better plan quality compared to manual forward planning and LGP inverse planning, with higher selectivity (0.73±0.07 vs 0.72±0.08 and 0.62±0.10), lower gradient index (2.71±0.25 vs 2.78±0.24 and 3.00±0.29), lower brainstem D0.1cc dose (6.10±4.46Gy vs 8.87±4.82Gy and 9.17±3.80Gy), and shorter total beam-on time (62.1±22.9min vs 83.6±28.2min and 70.7±16.7min). For the case that have six targets, compared with manual planning and LGP inverse planning, our MRL approach achieved higher selectivity (0.68 vs 0.57 and 0.47) and lower gradient index (3.77 vs 4.51 and 5.11). The beam-on time of our plan was slightly longer than manual planning and LGP inverse planning (206.4min vs 204.7min and 199.3min). We have also performed sector duration optimization at the isocenters determined by manual planning or the LGP inverse planning, and the resulting plan qualities were found to be inferior to our MRL approach for all the six cases. This preliminary study has demonstrated the efficacy and feasibility of our MRL inverse planning approach for GK radiosurgery.

Feasibility of automatic IMRT planning for cervical cancer based on a database of previously-treated patients

Objective To develop and evaluate an automatic intensity-modulated radiation therapy (IMRT) program for cervical cancer based on a database of overlap volume histogram (OVH) and high-quality cervical IMRT plans for previously-treated patients. Methods A database consisting of high-quality IMRT plans and OVHs from 200 cervical cancer patients was established. OVHs of another 26 cervical cancer patients were converted into gray level images to calculate the image similarity compared with those from the database. The planning optimization function of the patients from the database with the highest image similarity was selected and inherent Pinnacle3 scripts were utilized to automatically generate IMRT plan. Finally, the dosimetric parameters, plan quality and design time were statistically compared between the automatic and manual plans. Results The target coverage, conformity index and homogeneity index did not significantly differ between two plans (all P>0.05). The V40, V45 and mean dose for the rectum in the automatic plans were significantly decreased by 6.1%, 1.3% and 50.7 cGy than those in the manual plans (all P 0.05). The plan design time was shortened by 71% in the automatic plans. Conclusions The automatic IMRT plans based on a database of OVH and high-quality IMRT plans can not only significantly shorten the plan design time, but also reduce the irradiated dose of normal tissues without compromising the target coverage and conformity index. Key words: Cervical neoplasm/intensity-modulated radiation therapy; Overlap volume histogram; Automatic plan

Abstract P4-12-10: Clinical implementation of the machine learning-based automated treatment planning tool for whole breast radiotherapy

Abstract Objectives: The machine learning (ML)-based automated treatment planning tool has been developed and evaluated for whole breast radiotherapy. This study implemented the tool in clinic and compared plan quality and planning efficiency with the manual treatment process for whole breast radiotherapy using irregular surface compensator technique. Methods: This study involved two phases. The 1st phase was to evaluate and the 2nd phase was to implement in the clinic. In the 1st phase, thirty whole breast or chest wall cases were planned by using the irregular surface compensator technique with fluence maps manually and iteratively edited to achieve uniform dose distribution to the target by planners. Patients were treated with these manual plans after physician’s approval. The evaluation phase thus compared our in-house ML-based automated treatment planning tool implemented in Eclipse Scripting Application Programming Interface (ESAPI) to these manual plans. The ML-based planning tool generated the fluence maps with the same beam parameters such as beam energy, gantry angle, collimator angle, and aperture shape as manual plans. Breast or chest wall clinical target volume (CTV) coverage based on the percentage CTV volume receiving 95% of the prescribed dose (V95%) and high-dose volume based on V105% were compared to evaluate the plan quality as well as the planning efficiency. Two-tailed Wilcoxon Signed-Rank test was performed to test the null hypothesis that the two planning schemes yield equivalent plan quality. In the 2nd phase, the planners used the automated planning tool for fourteen plans (twelve patients) followed by manual fluence modification as needed. Physicians reviewed and approved the plans, and patients were treated. Results: For the 1st phase, the mean planning time was 110.2 min with standard deviation (SD) of 62.8 min for the manual planning with the range from 25 to 270 min, and 6.4 min with SD of 2.1 min for the automated planning with the range from 4 to 12 min (p&amp;lt;0.01). CTV mean V95% was 96.7% (SD: 5.0%) for the manual planning and 96.7% (SD: 4.8%) for the automated planning (p=0.89). CTV mean V105% was 21.6% (SD: 29.8%) for the manual planning and 20.4% (SD: 30.5%) for the automated planning (p=0.22). Differences in doses to heart and lungs were negligible between the paired plans as the two planning schemes used the same beam parameters. For the 2nd phase, the mean planning time was 16.4 min (SD: 9.1 min) and the mean time for additional manual editing was 12.7 min (SD: 12.5 min). The mean total treatment planning time was 29.1 min (SD: 14.8 min).). CTV mean V95% was 97.2% (SD: 4.2%) and mean V105% was 8.2 % (SD: 3.6%). The manual post modifications were added by the planners with intention to improve the target coverage or to reduce high doses, yet, the difference between the plans without and without the manual post modification was negligible. Conclusion: The ML-based automated treatment planning tool through Varian ESAPI has been successfully implemented for clinical use going through two phases of study. Abiding to the same plan quality as manual process, the automated tool significantly reduced the planning time as the ML-based tool automate the iterative fluence editing process. Citation Format: Sua Yoo, Yang Sheng, Rachel Blitzblau, Suzanne Catalano, Jay Morrison, Leigh O'Neill, Fangfang Yin, Q. Jackie Wu. Clinical implementation of the machine learning-based automated treatment planning tool for whole breast radiotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-12-10.

Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer

PurposeTo implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer. Material and methodsThe protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV1), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTVART) and to PTV1 respectively. PTVART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA). ResultsThe resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V30Gy and V40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel. ConclusionsThe KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.

Template-based automation of treatment planning in advanced radiotherapy: a comprehensive dosimetric and clinical evaluation

Despite the recent advanced developments in radiation therapy planning, treatment planning for head-neck and pelvic cancers remains challenging due to large concave target volumes, multiple dose prescriptions and numerous organs at risk close to targets. Inter-institutional studies highlighted that plan quality strongly depends on planner experience and skills. Automated optimization of planning procedure may improve plan quality and best practice. We performed a comprehensive dosimetric and clinical evaluation of the Pinnacle3 AutoPlanning engine, comparing automatically generated plans (AP) with the historically clinically accepted manually-generated ones (MP). Thirty-six patients (12 for each of the following anatomical sites: head-neck, high-risk prostate and endometrial cancer) were re-planned with the AutoPlanning engine. Planning and optimization workflow was developed to automatically generate “dual-arc” VMAT plans with simultaneously integrated boost. Various dose and dose-volume parameters were used to build three metrics able to supply a global Plan Quality Index evaluation in terms of dose conformity indexes, targets coverage and sparing of critical organs. All plans were scored in a blinded clinical evaluation by two senior radiation oncologists. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. Autoplanning was able to produce high-quality clinically acceptable plans in all cases. The main benefit of Autoplanning strategy was the improvement of overall treatment quality due to significant increased dose conformity and reduction of integral dose by 6–10%, keeping similar targets coverage. Overall planning time was reduced to 60–80 minutes, about a third of time needed for manual planning. In 94% of clinical evaluations, the AP plans scored equal or better to MP plans. Despite the increased fluence modulation, dose measurements reported an optimal agreement with dose calculations with a γ-pass-rate greater than 95% for 3%(global)-2 mm criteria. Autoplanning engine is an effective device enabling the generation of VMAT high quality treatment plans according to institutional specific planning protocols.

Automatic Planning for Nasopharyngeal Carcinoma Based on Progressive Optimization in RayStation Treatment Planning System.

Objective:To evaluate and quantify the planning performance of automatic planning (AP) with manual planning (MP) for nasopharyngeal carcinoma in the RayStation treatment planning system (TPS).Methods:A progressive and effective design method for AP of nasopharyngeal carcinoma was realized through automated scripts in this study. A total of 30 patients with nasopharyngeal carcinoma with initial treatment was enrolled. The target coverage, conformity index (CI), homogeneity index (HI), organs at risk sparing, and the efficiency of design and execution were compared between automatic and manual volumetric modulated arc therapy (VMAT) plans.Results:The results of the 2 design methods met the clinical dose requirement. The differences in D95 between the 2 groups in PTV1 and PTV2 showed statistical significance, and the MPs are higher than APs, but the difference in absolute dose was only 0.21% and 0.16%. The results showed that the conformity index of planning target volumes (PTV1, PTV2, PTVnd and PGTVnx+rpn [PGTVnx and PGTVrpn]), homogeneity index of PGTVnx+rpn, and HI of PTVnd in APs are better than that in MPs. For organs at risk, the APs are lower than the MPs, and the difference was statistically significant (P < .05). The manual operation time in APs was 83.21% less than that in MPs, and the computer processing time was 34.22% more.Conclusion:IronPython language designed by RayStation TPS has clinical application value in the design of automatic radiotherapy plan for nasopharyngeal carcinoma. The dose distribution of tumor target and organs at risk in the APs was similar or better than those in the MPs. The time of manual operation in the plan design showed a sharp reduction, thus significantly improving the work efficiency in clinical application.

Dose-volume Histogram Analysis of Knowledge-based Volumetric-modulated Arc Therapy Planning in Postoperative Breast Cancer Irradiation.

We evaluated the dosimetric profiles of manually generated volumetric-modulated arc therapy (VMAT) plans and performance of a commercial knowledge-based planning system (KBP) in treating breast cancer. We defined the manually generated VMAT plan as the manual plan (MP). Twenty MPs were generated for left-sided breast cancer patients who underwent breast-conserving surgery and used to develop a KBP training set. The other five patients were used for validation. The dosimetric parameters among MPs, tangential irradiation plans (TPs), and KBP-VMAT plans (KBP-Ps) were compared. D95 and homogeneity of the planning target volume (PTV) were significantly higher and greater in MPs and KBP-Ps than in TPs. Lung V20, V40 The Dmean for the left anterior descending artery was lower in MPs and KBP-Ps than in TPs. KBP could save time in generating VMAT plans. MPs and KBP-Ps could ensure higher dose uniformity of PTV than TPs. KBP could faster generate comparable MPs for breast cancer.

Computer-assisted planning for minimally invasive anterior two-thirds laser corpus callosotomy: A feasibility study with probabilistic tractography validation.

Anterior two-thirds corpus callosotomy is an effective palliative neurosurgical procedure for drug-refractory epilepsy that is most commonly used to treat drop-attacks. Laser interstitial thermal therapy is a novel stereotactic ablative technique that has been utilised as a minimally invasive alternative to resective and disconnective open neurosurgery. Case series have reported success in performing laser anterior two-thirds corpus callosotomy. Computer-assisted planning algorithms may help to automate and optimise multi-trajectory planning for this procedure. To undertake a simulation-based feasibility study of computer-assisted corpus callostomy planning in comparison with expert manual plans in the same patients. Ten patients were selected from a prospectively maintained database. Patients had previously undergone diffusion-weighted imaging and digital subtraction angiography as part of routine SEEG care. Computer-assisted planning was performed using the EpiNav™ platform and compared to manually planned trajectories from two independent blinded experts. Estimated ablation cavities were used in conjunction with probabilistic tractography to simulate the expected extent of interhemispheric disconnection. Computer-assisted planning resulted in significantly improved trajectory safety metrics (risk score and minimum distance to vasculature) compared to blinded external expert manual plans. Probabilistic tractography revealed residual interhemispheric connectivity in 1/10 cases following computer-assisted planning compared to 4/10 and 2/10 cases with manual planning. Computer-assisted planning successfully generates multi-trajectory plans capable of LITT anterior two-thirds corpus callosotomy. Computer-assisted planning may provide a means of standardising trajectory planning and serves as a potential new tool for optimising trajectories. A prospective validation study is now required to determine if this translates into improved patient outcomes.

Comprehensive Comparison of Progressive Optimization Algorithm Based Automatic Plan and Manually Planned Treatment Technique for Cervical-Thoracic Esophageal Cancers.

Purpose:The purpose of the present study was first to apply the progressive optimization algorithm based automatic volumetric modulated arc therapy (POA-VMAT) technology to accelerate and improve the radiotherapy of cervicothoracic esophageal cancer (CTEC). We comprehensive analyze the feasibility, normal tissue complication probability (NTCP) and dosimetric results of POA-VMAT, manual based VMAT and step-shoot intensity-modulated radiation therapy (IMRT) plans in the treatment of CTEC.Methods:Sixty patients with CTEC with or without concomitant chemotherapy at our institution between 2017 and 2019 were retrospectively identified. The manual 7field-IMRT (7f-IMRT), Single-arc-VMAT and Double-arc-VMAT (Single-Arc/Double-Arc) plans were generated in all cases. The POA-VMAT was designed using the automatic dual-arc VMAT technology of Pinnacle3 9.10 planning system based on progressive optimization algorithm. Specially, it includes the selection of treatment techniques, the running of automated planning scripts, and the evaluation of the final radiotherapy regimen. Subsequently, quantitative evaluation of plans was performed by means of standard dose–volume histograms, homogeneity index (HI) and conformity index (CI).Results:Target dose conformity of the 7f-IMRT plan was inferior to all plans, whereas the Double-Arc plan was slightly inferior to the POA-VMAT but superior to the Single-Arc and 7f-IMRT plan. The HI for 7f-IMRT, Single-Arc, Double-Arc and POA-VMAT were 0.17 ± 0.08, 0.28 ± 0.06, 0.29 ± 0.06 and 0.28 ± 0.03, respectively. For the NTCP results, there was significant statistical difference among POA-VMAT, IMRT and VMAT plans. The total MU was reduced by 48.3% and 42.1% in Single-Arc and POA-VMAT plans compare to IMRT plans.Conclusions:By comprehensive consideration, POA-VMAT efficiently generate acceptable treatment plans for CTEC without dose escalation to OARs and overall superior to manual planning which is a good option for treating CTEC.

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients.

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average difference in plan isocenter/borders, and in organ mean dose (prescribed dose: 14.4Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.