IT'S OFFICIAL: Radformation's ClearCheck Software saves, on average, 30 minutes per patient during plan evaluation process.


It’s always exciting to hear from ClearCheck super users that want to do a full study of our software, not only because it validates their satisfaction with our product, but because it shows new users that our software truly does what we say it does. University backed studies give us the opportunity to share all of our hard work in a verifiable way. We here at Radformation HQ thought this would be a perfect item for our premiere blog post.


Click here if you’d like to set up a free ClearCheck demo to integrate automated planning in your clinic!


Thomas Jefferson University Department of Radiation Oncology’s Study (found here) was presented at AAPM 2018 in Nashville Tennessee. This study concluded that “single manual iteration of dosimetric plan evaluation for H&N, prostate, and SBRT lung cases without ClearCheck took an average of 8.1±1.3, 5.6±0.9, and 6.5±0.7 minutes, respectively,” and that ClearCheck “ClearCheck displays the dosimetric indices instantaneously.” You can’t beat instantaneous!

The Barbara Ann Karmanos Cancer Institute at Wayne State University Study (found here) was published November 1, 2018 in the International Journal of Radiation Oncology and was presented at the 2018 ASTRO Annual Meeting in San Antonio, Texas. This study shows how ClearCheck offers “the ability to evaluate all planning goals simultaneously during plan creation [resulting] in more efficient plan optimization and realization of planning goals.”


See detailed posters with study results below.


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Planning efficiency study of a treatment planning based Plan Evaluation Software

P. Charpentier, M. Trager, M. Werner-Wasik, Y. Yu
Department of Radiation Oncology, Thomas Jefferson University Hospital


    Purpose

  • Planning efficiency (PE) and quality are essential to a safe, effective, and productive radiation oncology department
  • Simultaneously comparing multiple dosimetric criteria is challenging when performing DVH analysis
  • As number of structures increases, the complexity, likelihood for error, and time required for dosimetric evaluation also increase
  • ClearCheck is an FDA approved software by Radformation built directly into the Eclipse treatment planning system through use of ESAPI (Eclipse Scripting Application Programming Interface)
  • Quickly and intuitively displays dosimetric quantities from customizable templates with pass/fail criteria.
  • Pulls information directly from dose-volume data and RT structures; therefore, has no numerical discrepancy to manual dosimetric calculations and is less error-prone than manual calculations.

    Methods and Materials

  • A typical inverse-planning process can be seen in Figure 1
  • With a standard DVH analysis, step 2 can be cumbersome and time-consuming for plans with many structures, as well as not allowing simultaneous analysis of dosimetry metrics

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Figure 1: Flowchart of a typical inverse-planning process.

  • The time taken to evaluate various dose constraints (physician and/or protocol based) for 17 plans (10 H&N, 5 prostate, and 2 SBRT lung with 25-29, 11, and 14-15 constraints, respectively) was measured as an indication of PE without ClearCheck. Subsequently, ClearCheck templates with the same constraints were created for each treatment site and populated instantly by running the script.

    Results

  • The times taken for a single manual iteration of dosimetric plan evaluation for H&N, prostate, and SBRT lung cases without ClearCheck can be seen in Table 1
  • If any changes to the plan occurred after evaluation, dosimetric evaluation would be repeated iteratively, increasing planning time
  • ClearCheck displays dosimetric indices and pass/fail information instantaneously

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    Table 1: Times taken for a single manual iteration of dosimetric plan evaluation for various sites.

    Conclusion

  • A typical inverse-planning process can be seen in Figure 1
  • With a standard DVH analysis, step 2 can be cumbersome and time-consuming for plans with many structures, as well as not allowing simultaneous analysis of dosimetry metrics
  • The time taken to evaluate various dose constraints (physician and/or protocol based) for 17 plans (10 H&N, 5 prostate, and 2 SBRT lung with 25-29, 11, and 14-15 constraints, respectively) was measured as an indication of PE without ClearCheck. Subsequently, ClearCheck templates with the same constraints were created for each treatment site and populated instantly by running the script.

via AAPM.org



Implementation of an Automation Tool for Treatment Planning Constraint Designation and Plan Evaluation

Jay Burmeister1,2, Geoff Baran1, Todd Bossenberger1, Ahmad Hammoud1, Harriett Jaenisch1, Justin Kamp1, Brian Loughery2, Kathryn Masi1, Michael Dominello2
(1) Karmanos Cancer Institute, Detroit, MI (2) Wayne State University, School of Medicine, Detroit, MI

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PURPOSE / OBJECTIVE(s)

To verify the quality and safety of a radiotherapy treatment plan, it is essential to assure that dose constraints are met for individual structures. This is especially important within the inverse planning process.

Structure constraints defined for a variety of treatment site-specific target and normal tissue structures have previously been evaluated at our center via manual measurement from calculated dose statistics within the treatment planning system. This process is time consuming and prone to manual errors.

MATERIAL & METHODS

A novel commercial system was implemented to automate this process. Eight treatment planners retrospectively defined and evaluated plan constraints for a total of 64 patients representing 10 distinct organ sites.

The manual and automated processes were timed to evaluate efficiency. The accuracy of this system was then evaluated through the assessment of 861 structure constraints for these 64 patients.

RESULTS

No constraint deviations were observed when using the automated system. A total of 18/861 (2.1%) of the manually reported constraints differed by more than 1% from results from the automated system & TPS. Some were clinically relevant deviations, with 7 (0.8%) of them greater than 10%. Deviation frequencies are shown in Table 1.

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Table 1: Constraint deviations and frequency.

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Table 2: Automated and manual constraint evaluation times in minutes

Mean time for definition, evaluation, and documentation of these constraints was 6.2 and 1.9 minutes for manual and automated processes, respectively. Table 2 shows average times and time savings using the automated system by treatment site.

Due to increased evaluation simplicity and efficiency, the automated process led to a >30% increase in the number of constraints evaluated per plan. It also allows simple evaluation of complex metrics without additional planning requirements, such as the creation of structure contours from isodose surfaces to evaluate common plan quality indices.

SUMMARY / CONCLUSION

Automated structure constraint definition, evaluation, and documentation results in greater accuracy and safety, reducing the rate of clinically relevant errors (>10% deviation) in plan metric reporting from 0.8% to 0% in this study of over 800 structure constraints. Two stray contours were also identified by the automated system which could have had a substantial impact on plan quality.

The ability to evaluate all planning goals simultaneously during plan creation results in more efficient plan optimization and realization of planning goals.

Automation of this process facilitated an increase in the number of constraints evaluated in each plan while simultaneously saving an average of 4.3 minutes per plan optimization.

REFERENCES / ACKNOWLED-
GEMENTS

The authors would like to acknowledge software support from Radformation, Inc. which facilitated the research presented here.

via Redjournal.org