Application programming in C# environment with recorded user software interactions and its application in autopilot of VMAT/IMRT treatment planning

Automated treatment planning for liver cancer stereotactic body radiotherapy

PURPOSE

To evaluate the quality of fully automated stereotactic body radiation therapy (SBRT) planning based on volumetric modulated arc therapy, which can reduce the reliance on historical plans and the experience of dosimetrists.

METHODS

Fully automated re-planning was performed on twenty liver cancer patients, automated plans based on automated SBRT planning (ASP) program and manual plans were conducted and compared. One patient was randomly selected and evaluate the repeatability of ASP, ten automated and ten manual SBRT plans were generated based on the same initial optimization objectives. Then, ten SBRT plans were generated for another selected randomly patient with different initial optimization objectives to assess the reproducibility. All plans were clinically evaluated in a double-blinded manner by five experienced radiation oncologists.

RESULTS

Fully automated plans provided similar planning target volume dose coverage and statistically better organ at risk sparing compared to the manual plans. Notably, automated plans achieved significant dose reduction in spinal cord, stomach, kidney, duodenum, and colon, with a median dose of D2% reduction ranging from 0.64 to 2.85 Gy. R50% and Dmean of ten rings for automated plans were significantly lower than those of manual plans. The average planning time for automated and manual plans was 59.8 ± 7.9 min vs. 127.1 ± 16.8 min (- 67.3 min).

CONCLUSION

Automated planning for SBRT, without relying on historical data, can generate comparable or even better plan quality for liver cancer compared with manual planning, along with better reproducibility, and less clinically planning time.

Semiautomatic Treatment Planning for the Field-in-field Technique in Whole Brain Irradiation

Objectives

In radiation therapy, the field-in-field (FIF) technique is used to prevent the administration of unnecessarily high doses to reduce toxicity. Recently, the FIF technique has been used for whole brain irradiation (WBI). Using the FIF technique, the volume that receives a higher than prescribed dose (hotspot) can be largely reduced; however, the treatment planning requires time. Therefore, to reduce the burden on the treatment planners, we propose a semiautomatic treatment planning method for the FIF technique.

Methods

In the semiautomatic FIF technique, hotspot regions in a treatment plan without the FIF technique are identified three-dimensionally, and beams with blocks that cover the hotspot regions using a multileaf collimator (sub-beams) are automatically created. The sub-beams are added to the original plan, and weights are assigned based on the maximum dose of the original plan to decrease the doses in the hotspot regions. This method was applied to 22 patients previously treated with WBI, wherein treatment plans were originally created without the FIF technique.

Results

In the semiautomatic FIF plans, the hotspots almost disappeared. The dose to 95% of the volume and the volume receiving at least 95% of the prescribed dose in the planning target volume decreased by only 0.3% ± 0.2% and 0.0% ± 0.1%, respectively, on average compared with those in the original plan. The average semiautomatic FIF processing time was 28 ± 4 s.

Conclusions

The proposed method reduced the hotspot regions with a slight change in the target coverage.

Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy

PURPOSE

To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation.

METHODS AND MATERIALS

One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose-volume parameters and by blinded clinical evaluation of 3 expert clinicians.

RESULTS

KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum-PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P < .05).

CONCLUSIONS

The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.

A plan template‐based automation solution using a commercial treatment planning system

Purpose

The purpose of this study was to develop an auto‐planning platform to be interfaced with a commercial treatment planning system (TPS). The main goal was to obtain robust and high‐quality plans for different anatomic sites and various dosimetric requirements.

Methods

Monaco (Elekta, St. Louis, US) was the TPS in this work. All input parameters for inverse planning could be defined in a plan template inside Monaco. A software tool called Robot Framework was used to launch auto‐planning trials with updated plan templates. The template modifier external to Monaco was the major component of our auto‐planning platform. For current implementation, it was a rule‐based system that mimics the trial‐and‐error process of an experienced planner. A template was automatically updated by changing the optimization constraints based on dosimetric evaluation of the plan obtained in the previous trial, along with the data of the iterative optimization extracted from Monaco. Treatment plans generated by Monaco with all plan evaluation criteria satisfied were considered acceptable, and such plans would be saved for further evaluation by clinicians. The auto‐planning platform was validated for 10 prostate and 10 head‐and‐neck cases in comparison with clinical plans generated by experienced planners.

Results

The performance and robustness of our auto‐planning platform was tested with clinical cases of prostate and head and neck treatment. For prostate cases, automatically generated plans had very similar plan quality with the clinical plans, and the bladder volume receiving 62.5 Gy, 50 Gy, and 40 Gy in auto‐plans was reduced by 1%, 3%, and 5%, respectively. For head and neck cases, auto‐plans had better conformity with reduced dose to the normal structures but slightly higher dose inhomogeneity in the target volume. Remarkably, the maximum dose in the spinal cord and brain stem was reduced by more than 3.5 Gy in auto‐plans. Fluence map optimization only with less than 30 trials was adequate to generate acceptable plans, and subsequent optimization for final plans was completed by Monaco without further intervention. The plan quality was weakly dependent on the parameter selection in the initial template and the choices of the step sizes for changing the constraint values.

Conclusion

An automated planning platform to interface with Monaco was developed, and our reported tests showed preliminary results for prostate and head and neck cases.

Automatizing a nonscripting TPS for optimizing clinical workflow and reoptimizing IMRT/VMAT plans

The purpose of this study was to design a toolkit that interacts with the Monaco (Elekta AB, Stockholm, Sweden) treatment planning system (TPS) for optimization of intensity-modulated radiation therapy and volumetric-modulated arc therapy without the need for a dedicated application programming interface. Successful inverse planning of radiotherapeutic treatment depends on the tweaking of many parameters; a tool was thus needed to explore these parameters more exhaustively without significantly increasing planning time. The software that we used was based on an open-source library that mimics human interaction with Microsoft Windows applications. We developed a simple Autoflow software routine that analyzes and optimizes calculated plans by considering the relative impact of different cost functions and modifying constraints accordingly. It was also designed to change segmentation parameters to fit more complex treatments. The toolkit is publicly available for download at https://bitbucket.org/hgugmradiofisica/pymonaco/src/master/. A study of prostate cancer cases was conducted to compare automatically created plans with previously treated cases. The toolkit fully automated the radiotherapy planning procedure, allowing the TPS to calculate or optimize plans during nonworking hours. In the prostate study, the use of this tool reduced the dose to organs at risk with a negligible decrease in target coverage. This tool enables the efficient use of the TPS, allowing research and clinical applications to coexist without conflict. It provides consistency and efficiency throughout the treatment planning process, which may be of great value to clinics with few resources. The impact of this tool on clinical workflow is important, as it not only provides better efficiency, but also increases treatment quality.

Automated Field-In-Field (FIF) Plan Framework Combining Scripting Application Programming Interface and User-Executed Program for Breast Forward IMRT

Purpose:

To develop an one-click option on treatment planning system that enables for the automated breast FIF planning by combining the Eclipse Scripting application programming interfaces and user-executed programming in Windows.

Methods:

Scripting application programming interfaces were designed to promote automation in clinical workflow associated with radiation oncology. However, scripting cannot provide all functions that users want to perform. Thus, a new framework proposes to integrate the benefits of the scripting application and user-executed programming for the automated field-in-field technique. We adopted the Eclipse Scripting applications, which provide an interface between treatment planning system server and client and enable for running the executed program to create dose clouds and adjust the planning parameters such as multi-leaf collimator placements and monitor unit values. Importantly, all tasks are designed to perform with one-click option on treatment planning system, including the automated pushback of the proposed plan to the treatment planning system.

Results:

The plans produced from the proposed framework were validated against the manual field-in-field plans with 40 retrospective breast patient cases in planning efficiency and plan quality. The elapsed time for running the framework was less than 1 minute, which significantly reduced the manual multi-leaf collimator/monitor unit adjustment time. It decreased the total planning time by more than 50%, relative to the manual field-in-field planning. In dosimetric aspects, the mean and maximum dose of the heart, lung, and whole breast did not exceed 1% deviation from the manual plans in most patient cases, while maintaining the target dose coverage and homogeneity index inside the target volume. From numerical analysis, the automated plans were demonstrated to be sufficiently close to the manual plans.

Conclusion:

The combination of scripting applications and user-executed programming for automated breast field-in-field planning accomplished a significant enhancement in planning efficiency without degrading the plan quality, relative to the manual field-in-field procedure.

Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations

Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.