Patient anatomy-specific trade-offs between sub-clinical disease coverage and normal tissue dose reduction in head-and-neck cancer.

PURPOSE: Risk of subclinical disease decreases with increasing distance from the GTV in head- and-neck squamous cell carcinoma (HNSCC). Depending on individual patient anatomy, OAR sparing could be improved by reducing target coverage in regions with low risk of subclinical spread. Using automated multi-criteria optimization, we investigate patient-specific optimal trade-offs between target periphery coverage and OAR sparing. METHODS: VMAT plans for 39 HNSCC patients were retrospectively created following our clinical three-target-level protocol: high-risk (PTV1), intermediate-risk (PTV2, 5 mm expansion from PTV1), and elective (PTV3). A baseline plan fulfilling clinical constraints (D 99 % ≥95 % for all PTVs) was compared to three plans with reduced PTV2 coverage (goals: PTV2 D 99 % ≥90 % or 85 %, or no PTV2) at the outer edge of PTV2. Plans were compared on PTV D 99 %, OAR D mean, and NTCP (xerostomia/dysphagia). RESULTS: Trade-offs between PTV2 coverage and OAR doses varied considerably between patients. For plans with PTV2 D 99 % -goal 90 %, median PTV2 D 99 % was 91.5 % resulting in xerostomia (≥grade 4) and dysphagia (≥grade 2) NTCP decrease of median [maximum] 1.9 % [5.3 %] and 1.1 % [4.1 %], respectively, compared to nominal PTV2 D 99 % -goal 95 %. For PTV2 D 99 % -goal 85 % median PTV D 99 % was 87 % with NTCP improvements of 4.6 % [9.9 %] and 1.5 % [5.4 %]. For no-margin plans, PTV2 D 99 % decreased to 83.3 % with NTCP reductions of 5.1 % [10.2 %] and 1.4 % [6.1 %]. CONCLUSION: Clinically relevant, patient-specific reductions in OARs and NTCP were observed at limited cost in target under-coverage at the outermost PTV edge. Given the observed inter-patient variations, individual evaluation is warranted to determine whether trade- offs would benefit a specific patient.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness.

PURPOSE: Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus. METHODS: A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes. RESULTS: A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness. CONCLUSION: Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

A systematically compiled set of quantitative metrics to describe spatial characteristics of radiotherapy dose distributions and aid in treatment planning.

PURPOSE: Many quantitative metrics have been proposed in literature for characterization of spatial dose properties. The aim of this study is to work towards much-needed consensus in the radiotherapy community on which of these metrics to use. We do this by comparing characteristics of the metrics and providing a systematically selected set of metrics to comprehensively quantify properties of the spatial dose distribution. METHODS: We searched the literature for metrics to quantitatively evaluate dose conformity, homogeneity, gradient (overall and directional), and distribution and location of over- and under-dosed sub-volumes. For each spatial dose property, we compared the responses of its corresponding metrics to simulated dose variations in a virtual water phantom. Selection criteria were a metric's ability to describe simulated scenarios robustly and to be visualized in an intuitive way. RESULTS: We saw substantial differences in the responses of metrics to the simulated dose variations. Some conformity and homogeneity metrics were unable to quantify certain types of changes (e.g. target under-coverage). Others showed a large dependency on the shape and volume of targets and isodoses. Metric values differed between calculations in a static plan and in simulated full treatment courses including setup errors, especially for metrics quantifying distribution and location of hot and cold spots. We provide an Eclipse plugin script to calculate and visualize selected metrics. CONCLUSION: The selected set of metrics provides complementary and comprehensive quantitative information about the spatial dose distribution. This work serves as a step towards broader consensus on the use of spatial dose metrics.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans.

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Cone beam CT based dose calculation in the thorax region.

BACKGROUND AND PURPOSE: The limited image quality in Cone Beam CT (CBCT) stemming primarily from scattered radiation hinders accurate CBCT based dose calculation in radiotherapy. We investigated the use of a stoichiometric calibration for dose calculation on CBCT images of lung cancer patients. MATERIALS AND METHODS: CBCT calibrations were performed with thorax scan protocols, using a phantom with approximately the diameter of an average human thorax and a central cavity simulating the thoracic cavity. Thus scatter conditions resembling those in clinical thorax CBCT scans were simulated. A published stoichiometric parametrization was used. A treatment plan was simulated on CBCT and CT scans of an anthropomorphic phantom, the dose distributions were calculated, and clinically relevant DVH parameters were compared. Twelve lung cancer patients had surveillance CT scans (s-CT) taken twice during their treatment course in addition to daily setup CBCTs. Dose calculations were performed on the s-CTs and the corresponding CBCTs taken on the same day, and DVH parameters were compared. RESULTS: Eighty percent of CBCT DVH parameters found for the phantom were within ±1% of CT doses, and 98% were within ±3%. For patients, the median CT/CBCT dose difference was within ±2%, and 98% of DVH parameters were within ±4%. Minimum dose to the tumor was underestimated (median 1.9%) on CBCT, while maximum doses to most organs at risk were slightly overestimated. CONCLUSION: Direct dose calculations on CBCTs of lung cancer patients were feasible within ∼4% accuracy using a simple calibration method, which is easily implemented in a clinical setting.