Strategies for automatic online treatment plan reoptimization using clinical treatment planning system: A planning parameters study

Authors

  • Li Taoran,

    Corresponding author
    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Wu Qiuwen,

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Zhang You,

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Vergalasova Irina,

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Lee W. Robert,

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Yin Fang-Fang,

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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  • Wu Q. Jackie

    1. Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710
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Abstract

Purpose:

Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (∼2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning).

Methods:

Simulated treatments of 10 patients were reoptimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original planˈs fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency.

Results:

All online reoptimized plans were finished within ∼2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased the monitor units by 17%. Differences of only 2cc or less in OAR V50%/V70Gy/V76Gy were observed between 100 and 50 iterations.

Conclusions

: It is feasible to perform automatic online reoptimization in ∼2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patientˈs daily anatomy, delivery efficiency, and time allowed for plan adaptation.

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