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

Visually guided inspiration breath-hold facilitated with nasal high flow therapy in locally advanced lung cancer

Background and purpose Reducing breathing motion in radiotherapy (RT) is an attractive strategy to reduce margins and better spare normal tissues. The objective of this prospective study (NCT03729661) was to investigate the feasibility of irradiation of non-small cell lung cancer (NSCLC) with visually guided moderate deep inspiration breath-hold (IBH) using nasal high-flow therapy (NHFT). Material and methods Locally advanced NSCLC patients undergoing photon RT were given NHFT with heated humidified air (flow: 40 L/min with 80% oxygen) through a nasal cannula. IBH was monitored by optical surface tracking (OST) with visual feedback. At a training session, patients had to hold their breath as long as possible, without and with NHFT. For the daily cone beam CT (CBCT) and RT treatment in IBH, patients were instructed to keep their BH as long as it felt comfortable. OST was used to analyze stability and reproducibility of the BH, and CBCT to analyze daily tumor position. Subjective tolerance was measured with a questionnaire at 3 time points. Results Of 10 included patients, 9 were treated with RT. Seven (78%) completed the treatment with NHFT as planned. At the training session, the mean BH length without NHFT was 39 s (range 15-86 s), and with NHFT 78 s (range 29-223 s) (p = .005). NHFT prolonged the BH duration by a mean factor of 2.1 (range 1.1-3.9s). The mean overall stability and reproducibility were within 1 mm. Subjective tolerance was very good with the majority of patients having no or minor discomfort caused by the devices. The mean inter-fraction tumor position variability was 1.8 mm (-1.1-8.1 mm;SD 2.4 mm). Conclusion NHFT for RT treatment of NSCLC in BH is feasible, well tolerated and significantly increases the breath-hold duration. Visually guided BH with OST is stable and reproducible. We therefore consider this an attractive patient-friendly approach to treat lung cancer patients with RT in BH

Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy

BACKGROUND AND PURPOSE: In radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring. MATERIALS AND METHODS: Twenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R. RESULTS: The highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = -0.38), for surface DSC with absolute adaption time (R = -0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = -0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and -0.64, respectively). CONCLUSION: Surface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures

VOXSI : a voxelized single- and dual-energy CT scenario generator for quantitative imaging

BACKGROUND AND PURPOSE: Dedicated CT simulation models have the potential to investigate several acquisition, reconstruction, or post-processing parameters without giving any radiation dose to patients. A software program was developed for the simulation and the analysis of single-energy and dual-energy CT images. Simulation and analysis functionalities of the software are described. MATERIALS AND METHODS: In the software, named VOXSI (VOXelized CT SImulator), the X-ray source, user specified simulation geometry, CT setup and the detector energy response can be varied. CT image reconstructions can be performed with an implementation of the ASTRA toolbox. In the DECT post processing toolkit, GUI tools are provided to calculate effective atomic number, relative electron density, pseudo-monoenergetic images, and material map images. Quantitative CT number validation, based on a RMI 467 tissue characterization phantom model, was performed between experimental and simulated CT scans at three different X-ray tube potentials (80, 120, and 140 kVp) with a third generation CT scanner. RESULTS: Overall, a good agreement was found for the mean CT numbers of the RMI 467 inserts. For all energies, the maximum difference in CT numbers between experimental and simulated data was below 17 HU for the soft tissues and below 48 HU for the osseous tissues. CONCLUSION: The software's simulation algorithm showed a good agreement between the CT measurements and CT simulations of the RMI 467 phantom at different energies. The capabilities of the software are demonstrated by an elaborated dual-energy CT research example

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study

PurposeArtificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. MethodsClinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). ResultsThe best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. ConclusionWe have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions

A comparison study between single- and dual-energy CT density extraction methods for neurological proton monte carlo treatment planning

Monte Carlo proton dose calculations requires mass densities calculated from the patient CT image. This work investigates the impact of different single-energy CT (SECT) and dual-energy CT (DECT) to density conversion methods in proton dose distributions for brain tumours. Material and methods: Head CT scans for four patients were acquired in SECT and DECT acquisition modes. Commercial software was used to reconstruct DirectDensity((TM)) images in Relative Electron Densities (RED, ) and to obtain DECT-based pseudo-monoenergetic images (PMI). PMI and SECT images were converted to RED using piecewise linear interpolations calibrated on a head-sized phantom, these fits were referred to as "PMI2RED" and "CT2RED". Two DECT-based calibration methods ("Hunemohr-15it" and "Saito-15it") were also investigated. images were converted to mass-densities () to investigate differences and one representative patient case was used to make a proton treatment plan. Using CT2RED as reference method, dose distribution differences in the target and in five organs-at-risk (OARs) were quantified. Results: In the phantom study, Saito-15it and Hunemohr-15it produced the lowest root-mean-square error (0.7%) and DirectDensity((TM)) the highest error (2.7%). The proton plan evaluated in the Saito-15it and Hunemohr-15it datasets showed the largest relative differences compared to initial CT2RED plan down to -6% of the prescribed dose. Compared to CT2RED, average range differences were calculated: -0.1 +/- 0.3 mm for PMI2RED; -0.8 +/- 0.4 mm for Hunemohr-15it, and -1.2 +/- 0.4 mm for Saito-15it. Conclusion: Given the wide choice of available conversion methods, studies investigating the density accuracy for proton dose calculations are necessary. However, there is still a gap between performing accuracy studies in reference phantoms and applying these methods in human CT images. For this treatment case, the PMI2RED method was equivalent to the conventional CT2RED method in terms of dose distribution, CTV coverage and OAR sparing, whereas Hunemohr-15it and Saito-15it presented the largest differences