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Title: SU-F-T-100: Development and Implementation of a Treatment Planning Tracking System Into the Radiation Oncology Clinic

Abstract

Purpose: With increasing numbers of cancer patients being diagnosed and the complexity of radiotherapy treatments rising it’s paramount that patient plan development continues to stay fluid within the clinic. In order to maintain a high standard of care and clinical efficiency the establishment of a tracking system for patient plan development allows healthcare providers to view real time plan progression and drive clinical workflow. In addition, it provides statistical datasets which can further identify inefficiencies within the clinic. Methods: An application was developed utilizing Microsoft’s ODBC SQL database engine to track patient plan status throughout the treatment planning process while also managing key factors pertaining to the patient’s treatment. Pertinent information is accessible to staff in many locations, including tracking monitors within dosimetry, the clinic network for both computers and handheld devices, and through email notifications. Plans are initiated with a CT and continually tracked through planning stages until final approval by staff. Patient’s status is dynamically updated by the physicians, dosimetrists, and medical physicists based on the stage of the patient’s plan. Results: Our application has been running over a six month period with all patients being processed through the system. Modifications have been made to allow for newmore » features to be implemented along with additional tracking parameters. Based on in-house feedback, the application has been supportive in streamlining patient plans through the treatment planning process and data has been accumulating to further improve procedures within the clinic. Conclusion: Over time the clinic will continue to track data with this application. As data accumulates the clinic will be able to highlight inefficiencies within the workflow and adapt accordingly. We will add in new features to help support the treatment planning process in the future.« less

Authors:
; ; ; ;  [1]
  1. University of Texas HSC SA, San Antonio, TX (United States)
Publication Date:
OSTI Identifier:
22642346
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; IMPLEMENTATION; PARTICLE TRACKS; PATIENTS; PLANNING; RADIOTHERAPY

Citation Formats

Kabat, C, Cline, K, Li, Y, Ha, C, and Stathakis, S. SU-F-T-100: Development and Implementation of a Treatment Planning Tracking System Into the Radiation Oncology Clinic. United States: N. p., 2016. Web. doi:10.1118/1.4956236.
Kabat, C, Cline, K, Li, Y, Ha, C, & Stathakis, S. SU-F-T-100: Development and Implementation of a Treatment Planning Tracking System Into the Radiation Oncology Clinic. United States. doi:10.1118/1.4956236.
Kabat, C, Cline, K, Li, Y, Ha, C, and Stathakis, S. 2016. "SU-F-T-100: Development and Implementation of a Treatment Planning Tracking System Into the Radiation Oncology Clinic". United States. doi:10.1118/1.4956236.
@article{osti_22642346,
title = {SU-F-T-100: Development and Implementation of a Treatment Planning Tracking System Into the Radiation Oncology Clinic},
author = {Kabat, C and Cline, K and Li, Y and Ha, C and Stathakis, S},
abstractNote = {Purpose: With increasing numbers of cancer patients being diagnosed and the complexity of radiotherapy treatments rising it’s paramount that patient plan development continues to stay fluid within the clinic. In order to maintain a high standard of care and clinical efficiency the establishment of a tracking system for patient plan development allows healthcare providers to view real time plan progression and drive clinical workflow. In addition, it provides statistical datasets which can further identify inefficiencies within the clinic. Methods: An application was developed utilizing Microsoft’s ODBC SQL database engine to track patient plan status throughout the treatment planning process while also managing key factors pertaining to the patient’s treatment. Pertinent information is accessible to staff in many locations, including tracking monitors within dosimetry, the clinic network for both computers and handheld devices, and through email notifications. Plans are initiated with a CT and continually tracked through planning stages until final approval by staff. Patient’s status is dynamically updated by the physicians, dosimetrists, and medical physicists based on the stage of the patient’s plan. Results: Our application has been running over a six month period with all patients being processed through the system. Modifications have been made to allow for new features to be implemented along with additional tracking parameters. Based on in-house feedback, the application has been supportive in streamlining patient plans through the treatment planning process and data has been accumulating to further improve procedures within the clinic. Conclusion: Over time the clinic will continue to track data with this application. As data accumulates the clinic will be able to highlight inefficiencies within the workflow and adapt accordingly. We will add in new features to help support the treatment planning process in the future.},
doi = {10.1118/1.4956236},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: A treatment planning process tracker database with input forms and a TV-viewable display webpage was developed and implemented in our clinic to collect time data points throughout the process. Tracking plan times is important because it directly affects the patient quality of care. Simply, the longer a patient waits after their initial simulation CT for treatment to begin, the more time the cancer has to progress. The tracker helps to drive workflow through the clinic, while the data collected can be used to understand and manage the process to find and eliminate inefficiencies. Methods: The overall process steps trackedmore » are CT-simulation, mark patient, draw normal contours, draw target volumes, create plan, and review/approve plan. Time stamps for task completion were extracted and used to generate a set of clinic metrics, among which include average time for each step in the process split apart by type of treatment, average time to completion for plans started in a given week, and individual overall completion time per plan. Results: Trends have been tracked for fourteen weeks of clinical data (196 plans). On average, drawing normal contours and target volumes is taking 2–5 times as long as creating the plan itself. This is potentially an issue because it could mean the process is taking too long initially, and it could be forcing the planning step to be done in a short amount of time. We also saw from our graphs that there appears to be no clear trend on the average amount of time per plan week-to-week. Conclusion: A tracker of this type has the potential to provide insight into how time is utilized in our clinic. By equipping our dosimetrists, radiation oncologists, and physicists with individualized metric sets, the tracker can help provide visibility and drive workflow. Funded in part by CPRIT (RP140105).« less
  • Purpose: There is potentially a wide variation in plan quality for a certain disease site, even for clinics located in the same system of hospitals. We have used a prostate-specific knowledge-based planning (KBP) model as a quality control tool to investigate the variation in prostate treatment planning across a network of affiliated radiation oncology departments. Methods: A previously created KBP model was applied to 10 patients each from 4 community-based clinics (Clinics A, B, C, and D). The KBP model was developed using RapidPlan (Eclipse v13.5, Varian Medical Systems) from 60 prostate/prostate bed IMRT plans that were originally planned usingmore » an in-house treatment planning system at the central institution of the community-based clinics. The dosimetric plan quality (target coverage and normal-tissue sparing) of each model-generated plan was compared to the respective clinically-used plan. Each community-based clinic utilized the same planning goals to develop the clinically-used plans that were used at the main institution. Results: Across all 4 clinics, the model-generated plans decreased the mean dose to the rectum by varying amounts (on average, 12.5, 2.6, 4.5, and 2.7 Gy for Clinics A, B, C, and D, respectively). The mean dose to the bladder also decreased with the model-generated plans (5.4, 2.3, 3.0, and 4.1 Gy, respectively). The KBP model also identified that target coverage (D95%) improvements were possible for for Clinics A, B, and D (0.12, 1.65, and 2.75%) while target coverage decreased by 0.72% for Clinic C, demonstrating potentially different trade-offs made in clinical plans at different institutions. Conclusion: Quality control of dosimetric plan quality across a system of radiation oncology practices is possible with knowledge-based planning. By using a quality KBP model, smaller community-based clinics can potentially identify the areas of their treatment plans that may be improved, whether it be in normal-tissue sparing or improved target coverage. M. Matuszak has research funding for KBP from Varian Medical Systems.« less
  • Purpose: To compare radiation machine measurement data collected by the Imaging and Radiation Oncology Core at Houston (IROC-H) with institutional treatment planning system (TPS) values, to identify parameters with large differences in agreement; the findings will help institutions focus their efforts to improve the accuracy of their TPS models. Methods and Materials: Between 2000 and 2014, IROC-H visited more than 250 institutions and conducted independent measurements of machine dosimetric data points, including percentage depth dose, output factors, off-axis factors, multileaf collimator small fields, and wedge data. We compared these data with the institutional TPS values for the same points bymore » energy, class, and parameter to identify differences and similarities using criteria involving both the medians and standard deviations for Varian linear accelerators. Distributions of differences between machine measurements and institutional TPS values were generated for basic dosimetric parameters. Results: On average, intensity modulated radiation therapy–style and stereotactic body radiation therapy–style output factors and upper physical wedge output factors were the most problematic. Percentage depth dose, jaw output factors, and enhanced dynamic wedge output factors agreed best between the IROC-H measurements and the TPS values. Although small differences were shown between 2 common TPS systems, neither was superior to the other. Parameter agreement was constant over time from 2000 to 2014. Conclusions: Differences in basic dosimetric parameters between machine measurements and TPS values vary widely depending on the parameter, although agreement does not seem to vary by TPS and has not changed over time. Intensity modulated radiation therapy–style output factors, stereotactic body radiation therapy–style output factors, and upper physical wedge output factors had the largest disagreement and should be carefully modeled to ensure accuracy.« less
  • The explosion of new imaging technologies such as X ray computed tomography (CT), ultrasound (US), positron emission tomography (PET), and nuclear magnetic resonance imaging (NMR) has forced a major change in radiation therapy treatment planning philosophy and procedures. Modern computer technology has been wedded to these new imaging modalities, making possible sophisticated radiation therapy treatment planning using both the detailed anatomical and density information that is made available by CT and the other imaging modalities. This had forced a revolution in the way treatments are planned, with the result that actual beam configurations are typically both more complex and moremore » carefully tailored to the desired target volume. This increase in precision and accuracy will presumably improve the results of radiation therapy.« less
  • Purpose: The use of magnetic resonance imaging (MRI) in radiation oncology is expanding rapidly, and more clinics are integrating MRI into their radiation therapy workflows. However, radiation therapy presents a new set of challenges and places additional constraints on MRI compared to diagnostic radiology that, if not properly addressed, can undermine the advantages MRI offers for radiation treatment planning (RTP). The authors introduce here strategies to manage several challenges of using MRI for virtual simulation in external beam RTP. Methods: A total of 810 clinical MRI simulation exams were performed using a dedicated MRI scanner for external beam RTP ofmore » brain, breast, cervix, head and neck, liver, pancreas, prostate, and sarcoma cancers. Patients were imaged in treatment position using MRI-optimal immobilization devices. Radiofrequency (RF) coil configurations and scan protocols were optimized based on RTP constraints. Off-resonance and gradient nonlinearity-induced geometric distortions were minimized or corrected prior to using images for RTP. A multidisciplinary MRI simulation guide, along with window width and level presets, was created to standardize use of MR images during RTP. A quality assurance program was implemented to maintain accuracy and repeatability of MRI simulation exams. Results: The combination of a large bore scanner, high field strength, and circumferentially wrapped, flexible phased array RF receive coils permitted acquisition of thin slice images with high contrast-to-noise ratio (CNR) and image intensity uniformity, while simultaneously accommodating patient setup and immobilization devices. Postprocessing corrections and alternative acquisition methods were required to reduce or correct off-resonance and gradient nonlinearity induced geometric distortions. Conclusions: The methodology described herein contains practical strategies the authors have implemented through lessons learned performing clinical MRI simulation exams. In their experience, these strategies provide robust, high fidelity, high contrast MR images suitable for external beam RTP.« less