Scripting Application Programming Interface Research Articles

Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Background and purposeThe ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic. Materials and methodsOur planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases. ResultsOur team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard. ConclusionsOur automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.

Analysis of human errors in the operation of various treatment planning systems over a 10-year period.

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

Automated treatment planning for whole breast irradiation with individualized tangential IMRT fields.

This study aimed to develop and validate algorithms for automating intensity modulated radiation therapy (IMRT) planning in breast cancer patients, with a focus on patient anatomical characteristics. We retrospectively selected 400 breast cancer patients without lymph node involvement for automated treatment planning. Automation was achieved using the Eclipse Scripting Application Programming Interface (ESAPI) integrated into the Eclipse Treatment Planning System. We employed three beam insertion geometries and three optimization strategies, resulting in 3600 plans, each delivering a 40.05Gy dose in 15 fractions. Gantry angles in the tangent fields were selected based on a criterion involving the minimum intersection area between the Planning Target Volume (PTV) and the ipsilateral lung in the Beam's Eye View projection. ESAPI was also used to gather patient anatomical data, serving as input for Random Forest models to select the optimal plan. The Random Forest classification considered both beam insertion geometry and optimization strategy. Dosimetric data were evaluated in accordance with the Radiation Therapy Oncology Group (RTOG) 1005 protocol. Overall, all approaches generated high-quality plans, with approximately 94% meeting the acceptable dose criteria for organs at risk and/or target coverage as defined by RTOG guidelines. Average automated plan generation time ranged from 6 min and 37 s to 9 min and 22 s, with the mean time increasing with additional fields. The Random Forest approach did not successfully enable automatic planning strategy selection. Instead, our automated planning system allows users to choose from the tested geometry and strategy options. Although our attempt to correlate patient anatomical features with planning strategy using machine learning tools was unsuccessful, the resulting dosimetric outcomes proved satisfactory. Our algorithm consistently produced high-quality plans, offering significant time and efficiency advantages.

First experience with the Monaco treatment planning system scripting application programming interface

First experience with the Monaco treatment planning system scripting application programming interface

Automating the Treatment Planning Process for Volumetric Modulated Arc Therapy Craniospinal Irradiation

PurposeThe purpose of this work is to develop a method to automate the treatment planning process of craniospinal irradiation (CSI) using volumetric modulated arc therapy (VMAT). Methods and MaterialsTwo scripts were developed using the Eclipse Scripting Application Programming Interface (ESAPI) to perform auto-plan preparation and optimization. Ten patients (age, 5-44 years) previously treated at our institution with low dose VMAT-CSI (prescription of 12 Gy) prior to total body irradiation were selected to evaluate the efficacy of the proposed auto-planning process. Paired t-tests compared the dosimetric indices of the auto-plans to the manually generated clinical plans. All plans were normalized to 95% of PTV coverage with the prescription dose. Two physicians and one physicist were asked to evaluate the manual plans and auto-plans of each patient in a blinded retrospective review and to indicate clinical acceptability and which plans were preferred for treatment. ResultsCompared to the manual CSI plans, the auto plans obtained significant reductions in Dmean to the parotids, submandibular glands, larynx, thyroid, and significant reduction in the plan PTV Dmax and D0.03cc. The standard deviation range of the dosimetric parameters was greatly reduced for auto plans (range, 0.1-1.3 Gy) relative to manual plans (range, 0.4-5.9 Gy) indicating better plan consistency. Among the ten patients, the auto-plans were preferred over the manual plans 90% of the time by the reviewing experts. The required time for auto-planning was approximately 1 hour compared to estimated 4 or more hours for manual planning. ConclusionsReductions in planning time without sacrifices in plan quality were obtained using the auto-planning process compared with manual planning. Variation in plan quality was also reduced. The auto-planning scripts will be made freely available to other institutions and clinics.

Development of an automated treatment planning approach for lattice radiation therapy.

Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure of vertices segmented inside the tumor. LRT is typically used to treat patients with large tumor volumes with cytoreduction intent. Due to the geometric complexity of the target volume and the required dose distribution, LRT treatment planning demands additional resources, which may limit clinical integration. We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making. Sarcoma cancer patients with tumor volume between 100 cm3 and 1500 cm3 who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices were modeled by spheres segmented within the gross tumor volume (GTV) with 1cm/1.5cm/2cm diameters (LRT-1cm/1.5cm/2cm) and 2 to 5cm center-to-center distance on square lattices alternating along the superior-inferior direction. Organs at risk were modeled by subtracting the GTV from the body structure (body-GTV). The prescription dose was that 50% of the vertice volume should receive at least 20Gy in one fraction. The automated dose optimization included three stages. The vertices optimization objectives were refined during optimization according to their values at the end of the first and second stages. Lattices were classified according to a score based on the minimization of body-GTV max dose and the maximization of GTV dose uniformity (measured with the equivalent uniform dose [EUD]), GTV dose heterogeneity (measured with the GTV D90%/D10% ratio), and the number of patients with more than one vertex inserted in the GTV. Plan complexity was measured with the modulation complexity score (MCS). Correlations were assessed with the Spearman correlation coefficient (r) and its associated p-value. Thirty-three patients with GTV volumes between 150 and 1350 cm3 (median GTV volume=494 cm3 , IQR=272-779 cm3 were included. The median time required for segmentation/planning was 1min/21min. The number of vertices was strongly correlated with GTV volume in each LRT lattice for each center-to-center distance (r>0.85, p-values<0.001 in each case). Lattices with center-to-center distance=2.5cm/3cm/3.5cm in LRT-1.5cm and center-to-center distance=4cm in LRT-1cm had the best scores. These lattices were characterized by high heterogeneity (median GTV D90%/D10% between 0.06 and 0.19). The generated plans were moderately complex (median MCS ranged between 0.19 and 0.40). The automated LRT planning method allows for the efficacious generation of vertices arranged in an ordered lattice and the refinement of planning objectives during dose optimization, enabling the systematic evaluation of LRT dosimetry from various lattice geometries.

Clinical Implementation of an Automated IMRT/VMAT Treatment Planning Tool

To create an in-house automated treatment planning tool for IMRT/VMAT treatments and evaluate the dosimetric plan quality against manually generated plans. A scripting application programming interface is employed to interact with a commercial treatment planning system (TPS) to implement automatic plan evaluation and update optimization parameters by mimicking the human planning process. The automated planning performs in an iterative fashion until reaching an acceptable tradeoff among target coverage/dose homogeneity and sparing of critical organs at risk. In each iteration, the dose constraints, priorities, and optimization structures for are automatically updated based on the results of the current iteration. Twenty previously treated plans (10 prostate and 10 head and neck), were preliminarily used to evaluate the performance of the automated planning tool. The differences in target and organ-at-risk metrics from the manually generated clinical plans were analyzed using paired t-test to evaluate clinical acceptability of tour automated planning tool. The current in-house-developed automated planning solution is able to create plans for different disease sites, including head & neck, prostate, pelvis, and lung. So far, the VMAT plans for more than 150 different cases have been generated with the tool. The results for these were also evaluated. Compared to the manually generated clinical head and neck plans, all auto plans achieved PTV D95% coverage and critical organs at risk sparing without statistically significant change in average global Dmax (107.4% for manual vs 107.3% for automated plans). The auto-planning solution provided reduced maximum doses to brainstem and spinal cord (average reductions with standard deviations of 5.1 ± 2.6 Gy and 2.9 ± 1.4 Gy, respectively, all p <0.03), reduced average mean doses to contralateral parotid, ipsilateral parotid, contralateral submandibular gland, pharynx, esophagus, cochleae (reductions of 2.2 ± 2.9 Gy, 4.8 ± 4.7 Gy, 3.6 ± 5.2 Gy, 2.0 ± 7.1 Gy, 3.9 ± 2.6 Gy, 3.8 ± 5.0 Gy, respectively, all p < 0.045). Similar results were observed for the prostate plans. With the same PTV coverage and without statistically significant change in average global Dmax (106.5% for manual vs 106.8% for automated plans), the automated solution provided superior sparing for both bladder and rectum. Bladder V75, V70, V65 were reduced by 0.6% ± 2.1%, 0.8% ± 2.5%, and 0.9% ± 2.9% (all p <0.04), respectively. Rectum V75, V70, V65, V60 were reduced by 1.0% ± 2.3%, 1.2% ± 2.8%, 1.3% ± 3.2%, 1.6% ± 3.6% (all p < 0.01), respectively. Our automated treatment planning solution is capable of efficiently generating VMAT plans for different disease sites with superior dosimetric indices compared to manually generated plans. Our tool is integrated within a commercial TPS platform, so it has the advantage of seamless adoption into the standard workflow to improve plan quality and treatment planning efficiency in our clinic.

Application of the Integrated Deep Reinforcement Learning Solution in Automatic IMRT Planning for Gastric Cancer

The intensity-modulated radiation therapy (IMRT) treatment planning optimization process is usually a manual, tedious and time-consuming task. An unsupervised integrated automatic IMRT planning solution was discussed and implemented to simulate the manual operation during the whole planning process. Based on knowledge-based planning (KBP) and deep reinforcement learning (DRL) schemes, a novel integrated solution combining the multi-objectives optimization policy network (MOPN) and three-dimensional dose prediction module (3D-DPM) was proposed. The MOPN was trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS). The 3D-DPM was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOPN training. The Eclipse 15.6 TPS Scripting Application Programming Interface (ESAPI) was used to realize the automatic interaction between models and TPS. In this study, 100 previously treated gastric cancer cases were selected from the clinical database, 70 cases were used for the training of dose prediction model and MOPN, the remaining 30 cases for evaluating the feasibility and effectiveness of automatic treatment planning solution. For all tested cases, the complete automatic treatment plan for a new case was generated based on the integrated solution, with about 6 min. Compared with the MOPN initial plans, the actual dose of spinal cord, liver, kidney-left, kidney-right, and intestine in the MOPN final plans reduced 42.4%, 25.2%, 52.4%, 37.8%, 11.8% respectively. The dose result of OARs in the MOPN final plans was similar to those in the clinical plans. In addition, a new case was able to complete optimization with about 15 adjustment steps by the trained MOPN. We successfully developed an integrated solution for automatic treatment planning. It first includes the dose prediction module for obtaining patient-specific initial optimization objectives. We demonstrated that the trained MOPN can mimic the operations of the physicians to adjust multiple objectives and obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in clinical practice. Although improvement is warranted, the proposed integrated solution is a promising practical and effective approach for automatic planning in commercial TPS.

VMAT TBI Technique Using Autoplanning Scripts

Evident by the Children's Oncology Group (COG) survey of pediatric total body irradiation (TBI) practice, 100% of physicians were open to refining the conventional TBI technique to lower the lung dose and 75% of respondents were interested in implementing VMAT or a 3-D CRT and IMRT system TBI. Unfortunately, only a handful of COG institutions have implemented VMAT or Tomo TBI. We created the autoplanning scripts and shared them with the public to make the VMAT TBI more wide-spread. Here, we report on our initial experience of treating Stanford VMAT TBI technique using the scripts. From October 2019 to February 2023, 75 patients were treated with VMAT TBI at our institution. Organ sparing depended on the regimen: myeloablative (lungs, kidneys, and lenses); non-myeloablative (lungs, kidneys, lenses, gonads, brain, and thyroid). Treatment planning was performed using Eclipse Scripting Application Programming Interface (ESAPI) auto-planning scripts. Data on patient, treatment details, and dosimetric indices were collected. Treatment was delivered using IGRT for every isocenter and every fraction. In-vivo measurements were performed on the matchline between the VMAT and AP/PA fields and on the testes for the first fraction. Median patient age was 13 years (range, 1 - 64). The patient height and width range were 83.6-197.3 cm and 24.9-60.3 cm, respectively. Forty-eight percent of patients were treated using myeloablative and 52% nonmyeloablative regimens. For all patients the lungs, lungs-1cm, and kidneys Dmean were consistently spared to 59.8±4.7%, 43.0±6.5%, and 70.9±7.7% of the prescription dose, respectively. Gonadal sparing (Dmean = 31.5±5.9%) was achieved for all patients with benign disease. In addition, brain and thyroid was spared for these patients. For nine patients, VMAT TBI offered additional organ sparing (liver, heart, and previously irradiated areas) and possibility to perform the simultaneously integrated boost. The average PTV D1cc was 120.3±6.6% for all patients. PTV D1cc correlated with patient height and width (R2 = 0.62 and 0.53). Sixteen patients (21.3%) with height <116cm were treated with 3-isocenter VMAT only plans, 59 patients (78.6%) were treated with 3-4 isocenter VMAT plans in head-first-supine position and 1-3 AP/PA plan in feet-first-supine position. Rotational platform was used to change the patient orientation. The average matchline dose measurement indicated patient setup was reproducible (96.1±4.5% relative to planned dose). For the first 35 patients, treatment time, including patient setup and beam-on, was 47.5±9.5 min. VMAT TBI is a modern alternative to conventional 2D TBI treatment offering the possibility of organ sparing, dose painting, and accurate treatment using IGRT. The automated scripts enable streamline planning with consistent plan quality.

52 Automated Tangents-Only Treatment Planning for Breast Cancer Radiotherapy Using the Eclipse Scripting Application Programming Interface (ESAPI)

52 Automated Tangents-Only Treatment Planning for Breast Cancer Radiotherapy Using the Eclipse Scripting Application Programming Interface (ESAPI)

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy

PurposeWe previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Using deep reinforcement learning guided by human knowledge, we trained the VTP to autonomously adjust relevant parameters in treatment plan optimization, similar to a human planner, to generate high-quality plans for prostate cancer stereotactic body radiation therapy (SBRT). This study describes the clinical implementation and evaluation of VTP. Materials and methodsWe integrate VTP with Eclipse TPS using scripting Application Programming Interface. VTP observes dose-volume histograms of relevant structures, decides how to adjust dosimetric constraints, including doses, volumes, and weighting factors, and applies the adjustments to the TPS interface to launch the optimization engine. This process continues until a high-quality plan is achieved. We evaluated VTP’s performance using the prostate SBRT case from the 2016 American Association of Medical Dosimetrist/Radiosurgery Society plan study with its plan scoring system, and compared to human-generated plans submitted to the challenge. Using the same scoring system, we also compared the plan quality of 36 prostate SBRT cases (20 planned with IMRT and 16 planned with VMAT) treated at our institution for both VTP and human-generated plans. ResultsIn the plan study case, VTP achieved a score of 142.1/150.0, ranking the third in the competition (median 134.6). For the clinical cases, VTP achieved 110.6 ± 6.5 for 20 IMRT plans and 126.2 ± 4.7 for 16 VMAT plans, similar to scores of human-generated plans with 110.4 ± 7.0 for IMRT plans and 125.4 ± 4.4 for VMAT plans. The workflow, plan quality and planning time of VTP were reviewed to be satisfactory by experienced physicists. ConclusionWe successfully implemented VTP to operate a TPS for autonomous human-like treatment planning for prostate SBRT.

Mitigation of Intensity Modulated Radiation Therapy and Stereotactic Body Radiation Therapy Treatment Planning Errors on the Novel RefleXion X1 System Using Failure Mode and Effect Analysis Within Six Sigma Framework

The aim of this study was to apply the Six Sigma methodology and failure mode and effect analysis (FMEA) to mitigate errors in intensity modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT) treatment planning with the first clinical installation of RefleXion X1. The Six Sigma approach consisted of 5 phases: define, measure, analyze, improve, and control. The define, measure, and analyze phases consisted of process mapping and an FMEA of IMRT and SBRT treatment planning on the X1. The multidisciplinary team outlined the workflow process and identified and ranked the failure modes associated with the plan check items using the American Association of Physicists in Medicine Task Group 100 recommendations. Items with the highest average risk priority numbers (RPNs) and severity ≥7 were prioritized for automation using the Eclipse Scripting Application Programming Interface (ESAPI). The "improve" phase consisted of developing ESAPI scripts before the clinical launch of X1 to improve efficiency and safety. In the "control" phase, the FMEA ranking was re-evaluated 1 year after clinical launch. Overall, 100 plan check items were identified in which the RPN values ranged from 10.2 to 429.0. Fifty of these items (50%) were suitable for automation within ESAPI. Of the 10 highest-risk items, 8 were suitable for automation. Based on the results of the FMEA, 2 scripts were developed: Planning Assistant, used by the planner during preparation for planning, and Automated Plan Check, used by the planner and the plan checker during plan preparation for treatment. After 12 months of clinical use of the X1 and developed scripts, only 3 errors were reported. The average prescript RPN was 138.0, compared with the average postscript RPN of 47.8 (P < .05), signifying a safer process. Implementing new technology in the clinic can be an error-prone process in which the likelihood of errors increases with increasing pressure to implement the technology quickly. To limit errors in clinical implementation of the novel RefleXion X1 system, the Six Sigma method was used to identify failure modes, establish quality control checks, and re-evaluate these checks 1 year after clinical implementation.

Improved planning efficiency in multiple brain lesion SRS VMAT cases using Eclipse scripting.

Though dosimetry has a multitude of treatment modalities, software, and workflows to aid in the treatment planning process, treatment planners are still responsible for several tedious and monotonous tasks that could decrease their planning efficiency. The purpose of this study was to determine if scripting could improve treatment planning efficiency for multiple brain lesion stereotactic radiosurgery (SRS) volumetric arc therapy cases by reducing planning time commitment. A script was developed for multiple brain lesion SRS cases using Eclipse scripting application programming interface with the intention of improving treatment planning efficiency by creating optimization structures and importing prescription and suggested OS dose metrics to the optimizer. Nine treatment planners were each provided with 3 different multiple brain lesion, single-isocenter SRS cases. Each planner created 2 plans for each case. One of these 2 plans used the SRS script, and the other did not. There were 54 treatment plans developed, totaling 27 plan comparisons. Each of the 54 treatment plans were considered clinically acceptable based on the participating institution's plan quality guidelines. Statistical analyses of planning time commitment with and without the SRS script were performed using RStudio. The mean and median planning times with and without the SRS script were compared using a paired T-test and Wilcoxon Signed Rank test, respectively, and effect size was evaluated using Cohen's classification. Using the SRS script resulted in statistically significant reduction in total contouring time (11.3 vs 2.8 minutes, p < 0.001), optimizer preparation time (7.7 vs 2.1 minutes, p < 0.001), and overall planning time (105.1 vs 77.9 minutes, p < 0.001). This study concluded that scripts developed using Eclipse scripting application programming interface offer an opportunity to improve treatment planning efficiency by reducing the planning time commitment for treatment planners.

Automated testing platform for radiotherapy treatment planning scripts.

Realizing the potential of user-developed automation software interacting with a treatment planning system (TPS) requires rigorous testing to ensure patient safety and data integrity. We developed an automated test platform to allow comparison of the treatment planning database before and after the execution of a write-enabled script interacting with a commercial TPS (Eclipse, Varian Medical Systems, Palo Alto, CA) using the vendor-provided Eclipse Scripting Application Programming Interface (ESAPI). The C#-application known as Write-Enable Script Testing Engine (WESTE) serializes the treatment planning objects (Patient, Structure Set, PlanSetup) accessible through ESAPI, and then compares the serialization acquired before and after the execution of the script being tested, documenting identified differences to highlight the changes made to the treatment planning data. The first two uses of WESTE demonstrated that the testing platform could acquire and analyze the data quickly (<4 s per test case) and facilitate the clinical implementation of write-enabled scripts.

Development of independent dose verification plugin using Eclipse scripting API for brachytherapy.

In this study, an independent dose verification plugin (DVP) using the Eclipse Scripting Application Programming Interface (ESAPI) for brachytherapy was developed. The DVP was based on the general 2D formalism reported in AAPM-TG43U1. The coordinate and orientation of each source position were extracted from the translation matrix acquired from the treatment planning system (TPS), and the distance between the source and verification point (r) was calculated. Moreover, the angles subtended by the center-tip and tip-tip of the hypothetical line source with respect to the verification point (θ and β) were calculated. With r, θ, β and the active length of the source acquired from the TPS, the geometry function was calculated. As the TPS calculated the radial dose function, g(r), and 2D anisotropy function, F(r,θ), by interpolating and extrapolating the corresponding table stored in the TPS, the DVP calculated g(r) and F(r,θ) independently from equations fitted with the Monte Carlo data. The relative deviation of the fitted g(r) and F(r,θ) for the GammaMed Plus HDR 192Ir source was 0.5% and 0.9%, respectively. The acceptance range of the relative dose difference was set to ±1.03% based on the relative deviation between the fitted functions and Monte Carlo data, and the linear error propagation law. For 64 verification points from sixteen plans, the mean of absolute values of the relative dose difference was 0.19%. The standard deviation (SD) of the relative dose difference was 0.17%. The DVP maximizes efficiency and minimizes human error for the brachytherapy plan check.

Assessment of Knowledge-Based Planning Model in Combination with Multi-Criteria Optimization in Head-and-Neck Cancers.

Aim:The aim of this study was to build knowledge-based planning model (KBPM) for head-and-neck (HN) cancers using volumetric-modulated arc therapy (VMAT), optimized with multi-criteria optimization (MCO), and to evaluate KBPM plan quality with clinical plan (CP) using in-house developed Python script.Materials and Methods:Two hundred previously treated simultaneously integrated boost (SIB) HN VMAT plans (RapidArc®) were selected for creating KBPM. These plans were further optimized using MCO to strike right trade-off between target and organs at risk (OARs). The script was written using Python V3.7.1 to automatically extract and analyze treatment plan dosimetric parameters through Eclipse Scripting Application Programming Interface (ESAPI). Analyzed plans that met deliverable quality were modeled using regression-based KBPM framework. The trained model is validated with 35 cohorts of HN SIB patients.Results:MCO plans were able to improve the OAR sparing without compromising target coverage compared to user-optimized CPs except for increased heterogeneity. With MCO, spinal cord dose D0.03cc is reduced by 3.2 Gy ± 1.8 Gy, parotid mean dose by 2 Gy ± 1.7 Gy compared to CPs, respectively. MCO-based KBPM plans were comparable to CP with improved sparing for left and right parotids by 11.5% and 7.8%, respectively.Conclusion:MCO-based KBPM plans were superior to user plans in terms of OAR sparing and user need to spend more time to meet the model-based plan outcomes. Created KBPM planning is simple and efficient to generate estimate for OAR sparing to guide entry and intermittent planners to improve their clinical planning skills with lesser planning time. Python ESAPI is a powerful tool to extract plan parameters and quickly evaluate either individual or a cohort of plans.

Design of a 3D patient-specific collision avoidance virtual framework for half-gantry proton therapy system.

IntroductionThis study presents a comprehensive collision avoidance framework based on three‐dimension (3D) computer‐aided design (CAD) modeling, a graphical user interface (GUI) as peripheral to the radiation treatment planning (RTP) environment, and patient‐specific plan parameters for intensity‐modulated proton therapy (IMPT).MethodsA stand‐alone software application was developed leveraging the Varian scripting application programming interface (API) for RTP database object accessibility. The Collision Avoider software models the Hitachi ProBeat‐V half gantry design and the Kuka robotic couch with triangle mesh structures. Patient‐specific plan parameters are displayed in the collision avoidance software for potential proximity evaluation. The external surfaces of the patients and the immobilization devices are contoured based on computed tomography (CT) images. A “table junction‐to‐CT‐origin” (JCT) measurement is made for every patient at the time of CT simulation to accurately provide reference location of the patient contours to the treatment couch. Collision evaluations were performed virtually with the program during treatment planning to prevent four major types of collisional events: collisions between the gantry head and the treatment couch, gantry head and the patient's body, gantry head and the robotic arm, and collisions between the gantry head and the immobilization devices.ResultsThe Collision Avoider software was able to accurately model the proton treatment delivery system and the robotic couch position. Commonly employed clinical beam configuration and JCT values were investigated. Brain and head and neck patients require more complex gantry and patient positioning system configurations. Physical measurements were performed to validate 3D CAD model geometry. Twelve clinical proton treatment plans were used to validate the accuracy of the software. The software can predict all four types of collisional events in our clinic since its full implementation in 2020.ConclusionA highly efficient patient‐specific collision prevention program for scanning proton therapy has been successfully implemented. The graphical program has provided accurate collision detection since its inception at our institution.

Designing Robust and Automated Treatment Plans Utilizing Dynamic Couch Motion With Automated Treatment Planning: Delivering Dose to a New Dimension ― Part B

<h3>Purpose/Objective(s)</h3> To develop a novel treatment technique, with automated treatment planning, that will yield superior dose distribution with dynamic couch motion at static gantry positions. <h3>Materials/Methods</h3> Modern treatment planning systems (TPS) cannot design, calculate, or optimize treatment plans utilizing continuous couch-based delivery. Based on the current limitation of modern TPS, the need to plan dynamic couch-based dose delivery arises for our investigation. We implement an automatic method utilizing the TPS scripting application programming interface (ESAPI). Additionally, we introduce a novel gantry static couch motion technique (GsCM) with static gantry positions and dynamic couch motion for brain treatment. The script interface allows selection of PTV, adding MLCs and PTV margin, choosing the number of fields, and initiation of the dose calculation within the TPS environment. We can automatically design custom treatment plans with simple 3-dimentional (3D) planning and conformal arc. This script based automated planning was retrospectively applied to brain patients (n = 5) using 200 cGy for 23 fractions. Initial development required implementing a conversion from the TPS mechanical axes to that of the machine. The readable Extensible markup language (XML) files are generated in the TPS environment using ESAPI by combining the fields at each couch position into a single deliverable field for each gantry angle utilizing continuous couch motion. The entire workflow is feasible within our script interface. The last part of this study focused on verifying the dose delivery. We used an ion chamber array positioned in the vertical direction to avoid side beam entrance. Trajectory log files were also analyzed using root mean square error (RMSE) to verify the MLC, gantry, and couch position accuracy. <h3>Results</h3> All treatment plans satisfy the criteria of V100% ≥ 95% and respect the dose to organs-at-risk (OARs). Automated script-based plans took less than a minute (excluding dose calculation) to design as compared to manual planning (2-8 hours per plan). The delivery was found to be less than 2 minutes for two 90 degrees partial arcs as compared to field-by-field delivery (15-30 minutes based on the plan type). All script-based plans (n = 5) passed patient-specific QA (> 95%) using the criteria of 2 mm distance to agreement and 2% dose difference. The couch rotational RMSE error for step and shoot and continuous delivery was 0.1 degrees and 0.12 degrees, respectively. MLC RMSE was 0.005 mm, and 0.006 mm for step and shoot and continuous delivery. <h3>Conclusion</h3> The results of our study indicate that the automated custom planning with couch motion is feasible and comparable with clinical planning in terms of dosimetric quality. The developed methodology provides a tool that is integratable with the current TPS without any additional resources. It designs script-based plans for dynamic couch motion-based dose delivery.

Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning.

To develop and evaluate a practical automatic treatment planning method for intensity-modulated radiation therapy (IMRT) in cervical cancer cases. A novel algorithm named as Optimization Objectives Tree Search Algorithm (OOTSA) was proposed to emulate the planning optimization process and achieve a progressively improving IMRT plan, based on the Eclipse Scripting Application Programming Interface (ESAPI). 30 previously treated cervical cancer cases were selected from the clinical database and comparison was made between the OOTSA-generated plans and clinical treated plans and RapidPlan-based (RP) plans. In clinical evaluation, compared with plan scores of the clinical plans and the RP plans, 22 and 26 of the OOTSA plans were considered as clinically improved in terms of plan quality, respectively. The average conformity index (CI) for the PTV in the OOTSA plans was 0.86 ± 0.01 (mean ± 1 standard deviation), better than those in the RP plans (0.83 ± 0.02) and the clinical plans (0.71 ± 0.11). Compared with the clinical plans, the mean doses of femoral head, rectum, spinal cord and right kidney in the OOTSA plans were reduced by 2.34 ± 2.87 Gy, 1.67 ± 2.10 Gy, 4.12 ± 6.44 Gy and 1.15 ± 2.67 Gy. Compared with the RP plans, the mean doses of femoral head, spinal cord, right kidney and small intestine in the OOTSA plans were reduced by 3.31 ± 1.55 Gy, 4.25 ± 3.69 Gy, 1.54 ± 2.23 Gy and 3.33 ± 1.91 Gy, respectively. In the OOTSA plans, the mean dose of bladder was slightly increased, with 2.33 ± 2.55 Gy (versus clinical plans) and 1.37 ± 1.74 Gy (vs RP plans). The average elapsed time of OOTSA and clinical planning were 59.2 ± 3.47 min and 76.53 ± 5.19 min. The plans created by OOTSA have been shown marginally better than the manual plans, especially in preserving OARs. In addition, the time of automatic treatment planning has shown a reduction compared to a manual planning process, and the variation of plan quality was greatly reduced. Although improvement on the algorithm is warranted, this proof-of-concept study has demonstrated that the proposed approach can be a practical solution for automatic planning. The proposed method is novel in the emulation strategy of the physicists' iterative operation during the planning process. Based on the existing optimizers, this method can be a simple yet effective solution for automated IMRT treatment planning.

Portal dosimetry scripting application programming interface (PDSAPI) for Winston-Lutz test employing ceramic balls.

PurposeStereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments require a high degree of accuracy. Mechanical, imaging, and radiation isocenter coincidence is especially important. As a common method, the Winston‐Lutz (WL) test plays an important role. However, weekly or daily WL test can be very time consuming. We developed novel methods using Portal Dosimetry Scripting Application Programming Interface (PDSAPI) to facilitate the test as well as documentation.MethodsWinston‐Lutz PDSAPI was developed and tested on our routine weekly WL imaging. The results were compared against two commercially available software RIT (Radiological Imaging Technology, Colorado Springs, CO) and DoseLab (Varian Medical Systems, Inc. Palo Alto, CA). Two manual methods that served as ground truth were used to verify PDSAPI results. Twenty WL test image data sets (10 fields per tests, and 200 images in total) were analyzed by these five methods in this report.ResultsMore than 99.5% of WL PDSAPI 1D shifts agreed with each of four other methods within ±0.33 mm, which is roughly the pixel width of a‐Si 1200 portal imager when source to imager distance (SID) is at 100 cm. 1D shifts agreement for ±0.22 mm and 0.11 mm were 96% and 63%, respectively. Same trend was observed for 2D displacement.ConclusionsWinston‐Lutz PDSAPI delivers similar accuracy as two commercial applications for WL test. This new application can save time spent transferring data and has the potential to implement daily WL test with reasonable test time. It also provides the data storage capability, and enables easy access to imaging and shift data.