Treatment Management System Research Articles

Simulation-Omitted Replan with Cone Beam Computed Tomography based Adaptive Online Radiotherapy System ― Transferring Adapted Plan to Non-Adaptive Ring Gantry Linear Accelerator for Image Guided Radiotherapy

Artificial intelligence powered cone beam computed tomography (CBCT) based online adaptive radiotherapy (oART) system offers a streamlined and efficient process for daily ART as the default. In our prior work, we developed a workflow to utilize this oART system as simulation-omitted replan platform and treat the adapted plan on the oART system with image guided radiotherapy (IGRT) until next adaptation. However, the IGRT fractions will occupy the treatment slots of the machine. In this work, we aim to develop a semi-automatic workflow to allow the adapted plan to be treat on the non-adaptive ring gantry linear accelerator (non-ART Linac) and dedicate the oART system for adaptive treatments. The oART system and the non-ART Linac were machine-matched to the same representative beam data. In the oART system, the initial plan is setup as 'adaptive' treatment and patients are only treated on the oART system for adaptive replan. The IGRT fractions are all treated on the non-ART Linac. An API script was developed to automatically (1) grab the adapted DICOM plan files from the secondary calculation system and write directly back to the database of the treatment management system (TMS), (2) change the DICOM tags to make the files compatible in the TMS system, (3) insert the kV-CBCT field to make the plan deliverable in the non-ART Linac. There are minimum remaining manual steps to setup the number of fractions to the intended number of IGRT fractions and to link plan to the prescription in TMS. We compare the required resources and the percentage of ART treatments on the oART system before and after the implementation of the proposed workflow to quantify the improvement of service. The proposed workflow and automation eliminated the need to convert between IGRT/ART fractions in the Ethos system and reduced manual work by 25 minutes each adapted plan transfer. Table 1 summarizes the number of physics tasks and the percentage of ART fractions in oART system per month before and after the proposed workflow. This workflow reduced the physics IGRT/ART tasks from 107±31 to 65±21 tasks per month (p<0.05). Percentage of ART treatments on oART system increase from 30%±3% to 57%±13% (p<0.05). We also observed increased utilization of ART from 46% in the 1st month to 71% in the 6th month since it is easier to find a feasible time slot for the clinical team. The majority of the remaining IGRT on oART system are lung SBRT where the first fraction is not adapted due to being within a week of the simulation. Leveraging CBCT based ART system as replan platform and non-ART Linac as IGRT platform is clinically feasible. This process significantly improved the turnaround time for replan, reduced the required resource and promotes the utilization of oART.

Automated data abstraction for quality surveillance and outcome assessment in radiation oncology.

Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider.

SU‐E‐T‐138: Automated Chart Review Module Including Cross‐Vendor Data Transfer Verification Developed for IHE‐RO Initiative

Purpose:To follow the Integrating Healthcare Enterprise ‐ Radiation Oncology (IHE‐RO) initiative of proper cross‐vendor technology integration, an automated chart checker (ACC) was developed. ACC compares extracted data from an approved patient plan in the Eclipse treatment planning system (TPS) against data existing in the Mosaiq treatment management system (TMS). ACC automatically analyzes these parameters using built‐in quality checklists to provide further aid in chart review.Methods:Eclipse TPS data are obtained using Eclipse scripting API (ESAPI) while Mosaiq TMS data are obtained from a radiotherapy‐treatment‐planning (RTP) file. Using this information, ACC identifies TPS‐TMS discrepancies in 18 primary beam parameters including MU, energy, jaw positions, gantry angle, table angle, accessories, and bolus for up to 31 beams. Next, approximately 40 items from traditional quality checklists are evaluated such as prescription consistency, DRR graticule placement, plan approval status, global max dose, and dose tracking coefficients. Parameters were artificially modified to determine if ACC would detect an error in data transfer and to test each component of quality checklists.Results:Using ESAPI scripting and RTP file‐processing, ACC was able to properly aggregate data from TPS and TMS for up to 31 beams. Errors were artificially introduced into each plan parameter, and ACC was able to successfully detect all of them within seconds. Next, ACC was able to successfully detect mistakes in the chart by identifying deviations with its quality checklists, within seconds.Conclusion:ACC effectively addresses the potential issue of faulty cross‐vendor data transfer, as described by IHE‐RO. In addition, ACC was also able to detect deviations from its built‐in quality checklists. ACC is already an invaluable tool for efficient and standardized chart review and will continue to improve as its incorporated checklists become more comprehensive.

SU-D-BRD-03: Improving Plan Quality with Automation of Treatment Plan Checks

Purpose: To evaluate the effectiveness of an automated plan check tool to improve first-time plan quality as well as standardize and document performance of physics plan checks. Methods: The Plan Checker Tool (PCT) uses the Eclipse Scripting API to check and compare data from the treatment planning system (TPS) and treatment management system (TMS). PCT was created to improve first-time plan quality, reduce patient delays, increase efficiency of our electronic workflow, and to standardize and partially automate plan checks in the TPS. A framework was developed which can be configured with different reference values and types of checks. One example is the prescribed dose check where PCT flags the user when the planned dose and the prescribed dose disagree. PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user. A PDF report is created and automatically uploaded into the TMS. Prior to and during PCT development, errors caught during plan checks and also patient delays were tracked in order to prioritize which checks should be automated. The most common and significant errors were determined. Results: Nineteen of 33 checklist items were automated with data extracted with the PCT. These include checks formore » prescription, reference point and machine scheduling errors which are three of the top six causes of patient delays related to physics and dosimetry. Since the clinical roll-out, no delays have been due to errors that are automatically flagged by the PCT. Development continues to automate the remaining checks. Conclusion: With PCT, 57% of the physics plan checklist has been partially or fully automated. Treatment delays have declined since release of the PCT for clinical use. By tracking delays and errors, we have been able to measure the effectiveness of automating checks and are using this information to prioritize future development. This project was supported in part by P01CA059827.« less

SU-D-BRD-02: Auto Weekly - An Automated Online Weekly Chart Check System for Medical Physics

Purpose: Radiation therapy has evolved to become not only more precise and potent, but also more complicated to monitor and deliver. More rigorous and comprehensive quality assurance is needed to safeguard ever advancing radiation therapy. ICRU standards dictate that an ever growing set of treatment parameters are manually checked weekly by medical physicists. This “weekly chart check” procedure is laborious and subject to human errors or other factors. A computer-assisted chart checking process will enable more complete and accurate human review of critical parameters, reduce the risk of medical errors, and improve the efficiency. Methods: We developed a web-based software system that enables a thorough weekly quality assurance checks. In the backend, the software retrieves all machine parameters from a Treatment Management System (TMS) and compares them against the corresponding ones from the treatment planning system. They are also checked for validity against preset rules. The results are displayed as a web page in the front-end for physicists to review. Then a summary report is generated and uploaded automatically to the TMS as a record for weekly chart checking. Results: The software system has been deployed on a web server in our department’s intranet, and has been tested thoroughly bymore » our clinical physicists. A plan parameter would be highlighted when it is off the preset limit. The developed system has changed the way of checking charts with significantly improved accuracy, efficiency, and completeness. It has been shown to be robust, fast, and easy to use. Conclusion: A computer-assisted system has been developed for efficient, accurate, and comprehensive weekly chart checking. The system has been extensively validated and is being implemented for routine clinical use.« less

SU‐E‐J‐129: A Strategy to Consolidate the Image Database of a VERO Unit Into a Radiotherapy Management System

Purpose:To present a strategy to integrate the imaging database of a VERO unit with a treatment management system (TMS) to improve clinical workflow and consolidate image data to facilitate clinical quality control and documentation.Methods:A VERO unit is equipped with both kV and MV imaging capabilities for IGRT treatments. It has its own imaging database behind a firewall. It has been a challenge to transfer images on this unit to a TMS in a radiation therapy clinic so that registered images can be reviewed remotely with an approval or rejection record. In this study, a software system, iPump‐VERO, was developed to connect VERO and a TMS in our clinic. The patient database folder on the VERO unit was mapped to a read‐only folder on a file server outside VERO firewall. The application runs on a regular computer with the read access to the patient database folder. It finds the latest registered images and fuses them in one of six predefined patterns before sends them via DICOM connection to the TMS. The residual image registration errors will be overlaid on the fused image to facilitate image review.Results:The fused images of either registered kV planar images or CBCT images are fully DICOM compatible. A sentinel module is built to sense new registered images with negligible computing resources from the VERO ExacTrac imaging computer. It takes a few seconds to fuse registered images and send them to the TMS. The whole process is automated without any human intervention.Conclusion:Transferring images in DICOM connection is the easiest way to consolidate images of various sources in your TMS. Technically the attending does not have to go to the VERO treatment console to review image registration prior delivery. It is a useful tool for a busy clinic with a VERO unit.

Migration check tool: automatic plan verification following treatment management systems upgrade and database migration

Software upgrades of the treatment management system (TMS) sometimes require that all data be migrated from one version of the database to another. It is necessary to verify that the data are correctly migrated to assure patient safety. It is impossible to verify by hand the thousands of parameters that go into each patient's radiation therapy treatment plan. Repeating pretreatment QA is costly, time‐consuming, and may be inadequate in detecting errors that are introduced during the migration. In this work we investigate the use of an automatic Plan Comparison Tool to verify that plan data have been correctly migrated to a new version of a TMS database from an older version. We developed software to query and compare treatment plans between different versions of the TMS. The same plan in the two TMS systems are translated into an XML schema. A plan comparison module takes the two XML schemas as input and reports any differences in parameters between the two versions of the same plan by applying a schema mapping. A console application is used to query the database to obtain a list of active or in‐preparation plans to be tested. It then runs in batch mode to compare all the plans, and a report of success or failure of the comparison is saved for review. This software tool was used as part of software upgrade and database migration from Varian's Aria 8.9 to Aria 11 TMS. Parameters were compared for 358 treatment plans in 89 minutes. This direct comparison of all plan parameters in the migrated TMS against the previous TMS surpasses current QA methods that relied on repeating pretreatment QA measurements or labor‐intensive and fallible hand comparisons.PACS numbers: 87.55.T, 87.55.Qr

WE-E-BRB-10: DosCheck - an Electronic Chart Checking Tool for Dosimetrists.

In addition to treatment planning, dosimetrists have to prepare documentation and manually enter data in treatment management system (TMS) which did not transfer or setup automatically. The required documents and data are dependent on the disease site, treatment machine and clinical workflow. Errors and inconsistencies can cause redundant work, treatment delays and potentially treatment errors. To address these issues, an electronic checking software tool, DosCheck was clinically implemented to check the existence of necessary documentations and the integrity of manually-entered data. The purpose of this software is to reduce the frequency of human errors and to improve efficiency. DosCheck reads data and documents from 1) TMS, 2) Pinnacle TPS, and 3) DICOM plan files stored in a DICOM-RT PACS. It processes documents in Word and PDF format, treatment plan data in Pinnacle native format and DICOM format, and Mosaiq data in database records. The software cross-checks data accuracy and consistency by following rules that are pre-defined according to the clinical requirements and treatment sties. It interacts with dosimetrists and presents instantaneous results via graphical user interface. DosCheck has been implemented in C#. It performs a full check for a patient with 20 seconds. It has been clinically commissioned and is used daily by all dosimetrists at our institution. Retrospective analysis shows that DosCheck identifies 30% to 40% of previously reported dosimetrist human errors. Additional ∼30% errors are checked by other tools that could be integrated DosCheck in the near future. As an electronic data checking tool, DosCheck can obtain and process data and documents from multiple clinical computer systems in the radiation oncology department, and perform checks according to clinical rules. It is able to improve the accuracy and efficiency of clinical data and document process, and therefore to reduce any potential inconsistencies and errors.

SU-E-T-218: The IHE-RO Helper Tool: Demonstrating the Connectivity Issues Solved by IHE-RO.

To develop a Web-based application (IHE-RO Helper) to allow comprehensive review of the interconnectivity and interoperability of various radiotherapy devices established through testing sanctioned by the Integrating Healthcare Enterprise-Radiation Oncology (IHE-RO). IHE-RO is an initiative sponsored by ASTRO to improve the way computer based systems in radiation oncology share information using well-defined data exchange standards (DICOM / HL7). At the IHE-RO Connectathon events over the last 4 years, 11 vendors with 14 different products have successfully tested and identified solutions to connectivity problems in treatment planning, simulation and delivery. Because the test results are highly technical, the interconnectivity issues amongst the RT devices may get overlooked by the end users. The IHE-RO helper tool is designed to operate in simple clinical terms with queries and presentations organized based on treatment techniques and clinical features that are familiar to the practitioners. For example, if you are planning to purchase a treatment planning system capable of generating plans (e.g. Stereotactic treatments) and are concerned whether the TPS can successfully transfer such data to your treatment management system (TMS) and subsequently to your treatment delivery system (TDS), the IHE-RO Helper can identify the connectivity requirements and list vendors that have successfully passed an IHE-RO Connectathon and validated their solution to the specific requirements. The IHE-RO helper tool provides a graphical and textual user interface to effectively demonstrate the solved interconnectivity problems between TPS, TMS and TDS. A report is also provided that explains the interconnectivity problems and its solutions. The IHE-RO helper is an effective tool to clearly identify vendor products that are IHE-RO compliant, thereby encourages vendor participation in testing and validation. Such a tool will be invaluable in procurement of new equipment to ensure a priori interoperability with anticipated RT devices deployed in the clinic. This research and development project is supported by the Bankhead-Coley Cancer Research Program grant # RC1-09BW-09-26833.

Monte Carlo evaluation of a photon pencil kernel algorithm applied to fast neutron therapy treatment planning

When dedicated software is lacking, treatment planning for fast neutron therapy is sometimes performed using dose calculation algorithms designed for photon beam therapy. In this work Monte Carlo derived neutron pencil kernels in water were parametrized using the photon dose algorithm implemented in the Nucletron TMS (treatment management system) treatment planning system. A rectangular fast-neutron fluence spectrum with energies 0–40 MeV (resembling a polyethylene filtered p(41)+ Be spectrum) was used. Central axis depth doses and lateral dose distributions were calculated and compared with the corresponding dose distributions from Monte Carlo calculations for homogeneous water and heterogeneous slab phantoms. All absorbed doses were normalized to the reference dose at 10 cm depth for a field of radius 5.6 cm in a 30 × 40 × 20 cm3 water test phantom. Agreement to within 7% was found in both the lateral and the depth dose distributions. The deviations could be explained as due to differences in size between the test phantom and that used in deriving the pencil kernel (radius 200 cm, thickness 50 cm). In the heterogeneous phantom, the TMS, with a directly applied neutron pencil kernel, and Monte Carlo calculated absorbed doses agree approximately for muscle but show large deviations for media such as adipose or bone. For the latter media, agreement was substantially improved by correcting the absorbed doses calculated in TMS with the neutron kerma factor ratio and the stopping power ratio between tissue and water. The multipurpose Monte Carlo code FLUKA was used both in calculating the pencil kernel and in direct calculations of absorbed dose in the phantom.