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Review
. 2019 Jul-Aug;9(4):200-207.
doi: 10.1016/j.prro.2018.12.002. Epub 2018 Dec 15.

Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences

Adam D Yock  1 , Radhe Mohan  2 , Stella Flampouri  3 , Walter Bosch  4 , Paige A Taylor  2 , David Gladstone  5 , Siyong Kim  6 , Jason Sohn  7 , Robert Wallace  8 , Ying Xiao  9 , Jeff Buchsbaum  10
Affiliations
Review

Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences

Adam D Yock et al. Pract Radiat Oncol. 2019 Jul-Aug.

Abstract

Purpose: With external beam radiation therapy, uncertainties in treatment planning and delivery can result in an undesirable dose distribution delivered to the patient that can compromise the benefit of treatment. Techniques including geometric margins and probabilistic optimization have been used effectively to mitigate the effects of uncertainties. However, their broad application is inconsistent and can compromise the conclusions derived from cross-technique and cross-modality comparisons.

Methods and materials: Conventional methods to deal with treatment planning and delivery uncertainties are described, and robustness analysis is presented as a framework that is applicable across treatment techniques and modalities.

Results: This report identifies elements that are imperative to include when conducting a robustness analysis and describing uncertainties and their dosimetric effects.

Conclusion: The robustness analysis approach described here is presented to promote reliable plan evaluation and dose reporting, particularly during clinical trials conducted across institutions and treatment modalities.

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Conflict of interest statement

Conflict of Interest: Nothing to disclose, Dr. Yock, Dr. Mohan, Dr. Flampouri, Dr. Gladstone, Dr. Wallace, Dr. Xiao, and Dr. Buchsbaum. Dr. Bosch reports grants from National Cancer Institute, during the conduct of the study. Dr. Sohn reports a NIH/NCI R01 Grant CA187053–01A1. Dr. Kim reports other from Medical Tool and Technology, LLC, outside the submitted work; In addition, Dr. Kim has a patent Multi-Source Intensity-Modulated Radiation Beam Delivery System and Method issued, a patent System including computed tomography device for image guided treatment issued, a patent Upright CT system issued, a patent Personal Digital Assistant (PDA) with Retina Display for Intrafractional Motion Reduction as a Remote-Controlled and Self-Contained Audiovisual Biofeedback System pending, a patent Pressure device for reducing the movement of abdomen, device for controlling the remaining movement of abdomen and radiation treatment system issued, a patent Smart patient positioning system for radiotherapy pending, a patent Thermoplastic mask monitoring system using pressure sensors pending, a patent Patient alignment and immobilization device for head and neck radiation therapy pending, a patent Real-Time Web-based wireless visual guidance for radiotherapy pending, a patent Thru-Mouth-Breathing Airflow Control Systems for Breath-Hold Respiratory Motion pending, a patent Liquid-Based Brachytherapy Ultrasound QA Phantom pending, and a patent Dynamic Profile-Based 4D MRI pending. Mrs. Taylor reports grant support from the federal share of income earned by Massachusetts General Hospital Proton Therapy Research and Treatment Center, contract C06 CA059267 and Public Health Service grant CA180803 awarded by the Department of Health and Human Services National Cancer Institute.

Figures

Figure 1:
Figure 1:
Dose-volume histograms of two plans with similar static target coverage, but differing robustness. The black lines are the target DVH under the nominal dose distribution while the gray bands represent 1,000 example target DVHs that result from translations and rotations applied to the nominal plan. Prescription dose was 21 Gy.
Figure 2:
Figure 2:
Dose-volume histograms of a stereotactic radiosurgery target volume after 1,000 random rigid transformations. The large number of transformations provide a probabilistic description of the effects of errors for this example single-fraction treatment. Similar figures apply to conventionally fractionated treatments, in which case each curve will reflect an estimate of the cumulative dose delivered in one of many hypothetical treatment courses. The gray band is comprised of DVHs created from each of the 1,000 scenarios. The black lines are DVHs derived from the voxelized minimum and maximum doses. Note the discrepancy resulting from the order of determining the minimum or maximum dose and creating the DVH.
See this image and copyright information in PMC

References

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