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  • br Article history br Keywords br Robust optimization br

    2022-05-07


    Article history:
    Keywords:
    Robust optimization
    Head-and-neck cancer
    Normal tissue complication probability
    Adaptive radiotherapy
    Dose accumulation
    Volumetric modulated arc therapy 
    Background and purpose: To assess the potential of composite minimax robust optimization (CMRO) com-pared to planning target volume (PTV)-based optimization for head and neck cancer (HNC) patients trea-ted with volumetric modulated arc therapy (VMAT). Materials and methods: Ten HNC patients previously treated with a PTV-based VMAT plan were studied. In addition to the PTV-plan a VMAT plan was created with CMRO. For both plans an adapted planning strategy was also investigated, including a plan Chloramphenicol during the third week of treatment. The PTV-plans and CMRO-plans (adapted and non-adapted) were evaluated by means of the estimated actu-ally given dose (EAGD). Therefore, the dose was calculated on daily acquired CBCTs, mapped onto the planning CT and accumulated. The plans were compared by dosimetric parameters and normal tissue complication probabilities (NTCPs) for tube feeding dependence, grade 2–4 dysphagia and xerostomia. The accuracy of CBCT-based dose accumulation was further quantified by comparisons of dose accumu-lation on weekly verification CTs.
    Results: On average, CMRO significantly increased (1.5 Gy) the D98% of the EAGD to the clinical target vol-ume and significantly decreased the mean dose of the ipsilateral parotid (2.8 Gy), inferior pharynx con-strictor muscle (0.7 Gy) and the oral cavity (0.8 Gy). This translated into significantly reduced NTCP of tube feeding dependence (0.9%) and xerostomia (2.8%). The differences in EAGD derived from evaluation CTs or CBCTs were minimal.
    Conclusion: Minimax robust optimization led to improved target coverage and dose reduction in organs at risk in HNC patients treated with VMAT.
    The primary goal of radiotherapy is to adequately treat the clin-ical target volume (CTV) with a uniform dose. The CTV is however subject to geometrical variations and setup uncertainties. The tra-ditional approach to avoid underdosage during the fractionated treatment course is to apply a planning target volume (PTV) mar-gin around the CTV [1–3]. This approach should lead to adequate PTV coverage on the planned dose distribution and adequate cov-erage of the CTV during the treatment course.
    The use of a PTV margin is based on the assumption that the dose distribution is invariant. However, setup errors and geometri-cal variations affect the shape of the dose distribution, especially in the vicinity of density gradients. PTV-less optimization approaches have been described in literature that provide a superior balance between tumor control rate and normal tissue toxicity. These opti-mization approaches are referred to as robust optimization [4–10].
    E-mail address: [email protected] (D. Wagenaar). 1 These authors contributed equally to the research described in this manuscript.
    Two approaches to robust planning optimization have been introduced: probabilistic planning and minimax optimization [11,12]. The probabilistic approach consists of optimizing the expectation value of objectives based on an a priori probability density function of geometric errors. Recently, Witte et al. investi-gated the potential clinical benefit of PTV-less probabilistic plan-ning in a spherical phantom [10]. They demonstrated that an indentation of the 95% dose level by one third of the margin size at a strictly uniform dose distribution at prescription level is feasi-ble without sacrificing tumor dose confidence. Fontanaroza et al. demonstrated probabilistic planning for IMRT in head and neck cancer (HNC) patients and found improved organ-at-risk (OAR) sparing as compared to PTV-based plans, with comparable CTV coverage [5]. The minimax robust optimization approach instead optimizes the objective value in the worst-case based on a posi-tioning inaccuracy in different directions. In contrast to the proba-bilistic approaches in which a probability distribution is needed, the minimax robust optimization approach only requires informa-tion about the scenarios to include. Fredriksson et al. proposed an r> 72 Robust optimization of VMAT
    implementation of minimax robust optimization which aims to optimize the objective function of the physically realistic compos-ite worst-case scenario [13]. In a recent study, this composite min-imax robust optimization (CMRO) was found to give a sharper dose fall-off than other implementations [14].
    CMRO was previously evaluated in static and dynamic phan-toms [15,16]. Both studies demonstrated improved target unifor-mity compared to PTV-based planning methods, especially near heterogeneous density regions. Since these phantom studies have demonstrated great potential also for OAR sparing, we evaluated the CMRO in detail for adaptive photon therapy in HNC patients.