Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications.

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Corrigendum: Impressive reduction of brain metastasis radionecrosis after cabozantinib therapy in metastatic renal carcinoma: a case report and review of the literature.

[This corrects the article DOI: 10.3389/fonc.2023.1136300.].

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans.

Background and purpose: Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans. Materials and methods: A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans' sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate P R γ (3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans. Results: The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%. Conclusions: The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Impressive reduction of brain metastasis radionecrosis after cabozantinib therapy in metastatic renal carcinoma: A case report and review of the literature.

Introduction: Radionecrosis is a consequence of SRS (stereotactic radiosurgery) for brain metastases in 34% of cases, and if symptomatic (8%-16%), it requires therapy with corticosteroids and bevacizumab and, less frequently, surgery. Oncological indications are increasing and appropriate stereotactic adapted LINACs (linear accelerators) are becoming more widely available worldwide. Efforts are being made to treat brain radionecrosis in order to relieve symptoms and spare the use of active therapies. Case presentation: Herein, we describe a 65-year-old female patient presenting with brain radionecrosis 6 months after stereotactic radiotherapy for two brain metastatic lesions. Being symptomatic with headache and slow cognitive-motor function, the patient received corticosteroids. Because of later lung progression, the patient took cabozantinib. An impressive reduction of the two brain radionecrosis areas was seen at the brain MRI 2 months after the initiation of the angiogenic drug. Discussion: The high incidence of radionecrosis (2/2 treated lesions) can be interpreted by the combination of SRS and previous ipilimumab that is associated with increased risk of radionecrosis. The molecular mechanisms of brain radionecrosis, and its exact duration in time, are poorly understood. We hypothesize that the antiangiogenic effect of cabozantinib may have had a strong effect in reducing brain radionecrosis areas. Conclusion: In this clinical case, cabozantinib is associated with a fast and significant volume reduction of brain radionecrosis appearing after SRS and concomitant immunotherapy. This drug seems to show, like bevacizumab, clinical implications not only for its efficacy in systemic disease control but also in reducing brain radionecrosis. More research is needed to evaluate all molecular mechanisms of brain radionecrosis and their interaction with systemic therapies like third-generation TKIs.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy.

BACKGROUND AND PURPOSE: Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis. MATERIALS AND METHODS: A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (Mt), while the other one at achieving the highest mean sample quality (Rt). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored. RESULTS: Both update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [-0.2 %;-14.7 %] while plan complexity showed only minor changes. Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability. CONCLUSIONS: Repeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

Applicability and usage of dose mapping/accumulation in radiotherapy.

Dose mapping/accumulation (DMA) is a topic in radiotherapy (RT) for years, but has not yet found its widespread way into clinical RT routine. During the ESTRO Physics workshop 2021 on "commissioning and quality assurance of deformable image registration (DIR) for current and future RT applications", we built a working group on DMA from which we present the results of our discussions in this article. Our aim in this manuscript is to shed light on the current situation of DMA in RT and to highlight the issues that hinder consciously integrating it into clinical RT routine. As a first outcome of our discussions, we present a scheme where representative RT use cases are positioned, considering expected anatomical variations and the impact of dose mapping uncertainties on patient safety, which we have named the DMA landscape (DMAL). This tool is useful for future reference when DMA applications get closer to clinical day-to-day use. Secondly, we discussed current challenges, lightly touching on first-order effects (related to the impact of DIR uncertainties in dose mapping), and focusing in detail on second-order effects often dismissed in the current literature (as resampling and interpolation, quality assurance considerations, and radiobiological issues). Finally, we developed recommendations, and guidelines for vendors and users. Our main point include: Strive for context-driven DIR (by considering their impact on clinical decisions/judgements) rather than perfect DIR; be conscious of the limitations of the implemented DIR algorithm; and consider when dose mapping (with properly quantified uncertainties) is a better alternative than no mapping.

Quantitative assessment of helical tomotherapy plans complexity.

PURPOSE: An unnecessary amount of complexity in radiotherapy plans affects the efficiency of the treatments, increasing the uncertainty of dose deposition and its susceptibility to anatomical changes or setup errors. To date, tools for quantitatively assessing the complexity of tomotherapy plans are still limited. In this study, new metrics were developed to characterize different aspects of helical tomotherapy (HT) plans, and their actual effectiveness was investigated. METHODS: The complexity of 464 HT plans delivered on a Radixact platform was evaluated. A new set of metrics was devised to assess beam geometry, leaf opening time (LOT) variability, and modulation over space and time. Sixty-five complexity metrics were extracted from the dataset using the newly in-house developed software library TCoMX: 29 metrics already proposed in the literature and 36 newly developed metrics. Their reciprocal relation is discussed. Their effectiveness was evaluated through correlation analyses with patient-specific quality assurance (PSQA) results. RESULTS: An inverse linear relation was found between the average number of closed leaves and the average number of MLC openings and closures as well as between the choice of the modulation factor and the discontinuity of the field, suggesting some intrinsic link between the LOT distribution and the geometrical complexity of the MLC openings. The newly proposed metrics were at least as correlated as the existing ones to the PSQA results. Metrics describing the geometrical complexity of the MLC openings showed the strongest connection to the PSQA results. Significant correlations were found between at least one of the new metrics and the γ index passing rate P R γ % ( 3 % G , 2 mm ) $P{R}_{\gamma}\%(3\%G,2\textit{mm})$ for six out of seven groups of plans considered. CONCLUSION: The new metrics proposed were shown to be effective to characterize more comprehensively the complexity of HT plans. A software library for their automatic extraction is described and made available.

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.

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report.

PURPOSE: This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS. METHODS: The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (ALcs) and tolerance limit (TLcs) were calculated for our clinic and monitored during the analysed period. RESULTS: The mean values ​​(± 1 standard deviation) of GP% (3%/2 mm) (both global and local normalization) are: 97.6% and 90.9%, respectively. The proposed ALcs and TLcs, after a period of two years' process monitoring are 89.4% and 91.1% respectively. CONCLUSIONS: The phantom measurements closely match the planned dose distributions, demonstrating that the calculation accuracy of the new Precision TPS and the delivery accuracy of the Radixact unit are adequate, with respect to international guidelines and reports. Furthermore, a first correlation with the planning parameters was made. Action and tolerance limits have been set for the new Radixact Linac.

Value of 18F-fluorocholine PET/CT in predicting response to radical radiotherapy in patients with localized prostate cancer.

PURPOSE: This study aims to establish whether metabolic parameters obtainable from FCH PET/CT can predict long-term response to radical radiotherapy (rRT) in patients with localized prostate cancer (PCa). METHODS: Drawing on a single-center database, we retrospectively reviewed the pre-treatment FCH PET/CT scans of 50 patients who underwent rRT between 2012 and 2017. Patients were enrolled if they had a follow-up of at least 3 years after rRT. Various metabolic parameters were considered for each PET/CT, including FCH multifocality. rRT was administered to all patients for a total equivalent dose of 76-80 Gy, using a standard or hypofractionated schedule. Patients were classified as disease-free (DF) if their PSA levels after rRT rose by <2 ng/mL vis-à-vis their PSA nadir, or as not disease free (NDF) if their PSA levels rose by more than 2 ng/ml. RESULTS: A multifocal FCH uptake in the prostate gland was identified in 27 patients (54%). At 3-year follow-up, 37 patients (74%) were judged DF, and 13 (26%) were NDF. The SUVmax and SUVmean, and the sum of the two values in all FCH foci in the prostate gland were significantly higher for NDF patients than for DF patients (all p < 0.005). The sum of the TLCKA levels in all FCH foci was likewise significantly higher in patients who were NDF than in those found DF (median 54.5 vs. 29.4; p < 0.05). At univariate analysis, the most of PET-metrics and Gleason Score were predictors of biochemical relapse after 3-year follow-up (all p < 0.05). CONCLUSION: Higher SUVs seems predict a worse outcome for patients with multifocal intraprostatic lesions who are candidates for rRT.

The influence of basic plan parameters on calculated small field output factors - A multicenter study.

PURPOSE: The influence of basic plan parameters such as slice thickness, grid resolution, algorithm type and field size on calculated small field output factors (OFs) was evaluated in a multicentric study. METHODS AND MATERIALS: Three computational homogeneous water phantoms with slice thicknesses (ST) 1, 2 and 3 mm were shared among twenty-one centers to calculate OFs for 1x1, 2x2 and 3x3 cm2 field sizes (FSs) (normalized to 10x10 cm2 FS), with their own treatment planning system (TPS) and the energy clinically used for stereotactic body radiation therapy delivery. OFs were calculated for each combination of grid resolution (GR) (1, 2 and 3 mm) and ST and finally compared with the OFs measured for the TPS commissioning. A multivariate analysis was performed to test the effect of basic plan parameters on calculated OFs. RESULTS: A total of 509 data points were collected. Calculated OFs are slightly higher than measured ones. The multivariate analysis showed that Center, GR, algorithm type, and FS are predictive variables of the difference between calculated and measured OFs (p < 0.001). As FS decreases, the spread in the difference between calculated and measured OFs became larger when increasing the GR. Monte Carlo and Analytical Anisotropic Algorithms, presented a dependence on GR (p < 0.01), while Collapsed Cone Convolution and Acuros did not. The effect of the ST was found to be negligible. CONCLUSIONS: Modern TPSs slightly overestimate the calculated small field OFs compared with measured ones. Grid resolution, algorithm, center number and field size influence the calculation of small field OFs.

Optimizing radiotherapy plans for cancer treatment with Tensor Networks.

We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the intensity-modulated radiation therapy technique-that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs-is based on the optimization problem of the beamlets intensities that shall result in a maximal delivery of the therapy dose to cancer while avoiding the organs at risk of being damaged by the radiation. The resulting optimization problem is expressed as a cost function to be optimized. Here, we map the cost function into an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we solve the dose optimization problem by finding the ground-state of the Hamiltonian using a Tree Tensor Network algorithm. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.

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.

Limiting treatment plan complexity by applying a novel commercial tool.

PURPOSE: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. METHODS: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. RESULTS: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. CONCLUSIONS: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.

Effectiveness of Cone Beam Computed Tomography Imaging During Radiation Therapy for the Detection of Initial Coronavirus Lung Disease 2019.

In this unique historic period afflicted by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, radiation therapy treatments cannot be delayed or suspended. We report the case of a 73-year-old woman with recently diagnosed extensive-stage small cell lung cancer with metastatic liver and bone lesions. A SARS-CoV-2 test was performed upon hospital admission and was negative. After 5 days she underwent radiation therapy on T6 and T11 with single fractions of 8 Gy each. Before treatment a cone beam computed tomography (CBCT) scan was performed to check the setup of the patient. Some suspected lung areas of ground glass opacities (GGOs) were clearly visible in the CBCT without any counterpart in the previous computed tomography (CT) simulation scan 3 days before. A new high-quality chest CT scan confirmed the previously suspected GGOs. The exam revealed multiple bilateral areas of subpleural GGOs, which are the primary findings on CT scan in the early phases of coronavirus disease 2019 (COVID-19) lung infection, in addition to pleural effusions, a finding that may occur as a complication of COVID-19. The patient then urgently repeated the SARS-CoV-2 test, which was positive and confirmed the infection. In conclusion, daily CBCT can be effective for early detection of COVID-19 lung disease in asymptomatic or mildly symptomatic patients.

Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study.

PURPOSE: To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. METHODS AND MATERIALS: Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms' accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. RESULTS: Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. CONCLUSIONS: The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.

Plan quality improvement by DVH sharing and planner's experience: Results of a SBRT multicentric planning study on prostate.

PURPOSE: To evaluate, in a multi-institutional context, the role of Dose Volume Histogram (DVH) sharing in order to achieve higher plan quality, to harmonize prostate Stereotactic Body Radiation Therapy (SBRT) plans and to assess if the planner's experience in SBRT could lead to lower dose at organs at risk (OARs). METHODS: During the first phase five patients enrolled for prostate SBRT were planned by multiple physicists according to common protocol. The prescription dose was 35 Gy in 5 fractions. Dosimetric parameters, modulation index (MIt), plan parameters, and planner experience level (EL) were statistically analyzed. During the second phase median DVHs from all centers were shared and physicists replanned one patient of the five, aiming at inter-planner harmonization and further OARs sparing. Data were summarized by Spearman-correlogram (p < 0.05) and boxplots. The Kruskal-Wallis test was used to compare the re-plans to the original plans. RESULTS: Seventy-eight SBRT plans from 13 centers were evaluated. EL correlated with modulation of plan parameters and reduction of OARs doses, such as volume receiving 28 Gy of rectum (rectum-V28Gy), rectum-V32Gy, and bladder-V30Gy. The re-plans showed significant reduced variability in rectum-V28Gy and increased PTV dose homogeneity. No significant difference in plan complexity metrics and plan parameters between plans and re-plans were obtained. CONCLUSIONS: Planner's experience in prostate SBRT was correlated with dosimetric parameters. Sharing median DVHs reduced variability among centers whilst keeping the same level of plan complexity. SBRT planning skills can benefit from a replanning phase after sharing DVHs from multiple centers, improving plan quality and concordance among centers.

Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution.

PURPOSE: This study measured to which extent RapidPlan can drive a reduction of the human-caused variability in prostate cancer treatment planning. METHODS: Seventy clinical prostate plans were used to train a RapidPlan model. Seven planners, with different levels of planning experience, were asked to plan a VMAT treatment for fifteen prostate cancer patients with and without RapidPlan assistance. The plans were compared on the basis of target coverage, conformance and OAR sparing. Inter-planner and intra-planner variability were assessed on the basis of the Plan Quality Metric formalism. Differences in mean values and InterQuartile Ranges between patients and operators were assessed. RESULTS: RapidPlan-assisted plans matched manual planning in terms of target coverage, homogeneity, conformance and bladder sparing but outperformed it for rectum and femoral heads sparing. 8 out of 15 patients showed a statistically significant increase in overall quality. Inter-planner variability is reduced in RapidPlan-assisted planning for rectum and femoral heads while bladder variability was constant. The inter-planner variability of the overall plan quality, IQR of PQM%, was approximately halved for all patients. RapidPlan assistance induced a larger increase in plan quality for less experienced planners. At the same time, a reduction in intra-planner variability is measured with a significant overall reduction. CONCLUSIONS: The assistance of RapidPlan during the optimization of treatments for prostate cancer induces a significant increase of plan quality and a contextual reduction of plan variability. RapidPlan is proven to be a valuable tool to leverage the planning skills of less experienced planners ensuring a better homogeneity of treatment plan quality.