Treatment planning and delivery practice of lung SBRT: Results of the 2022 ESTRO physics survey.

BACKGROUND AND PURPOSE: The use of Stereotactic Body Radiation Therapy (SBRT) in lung cancer is increasing. However, there is no consensus on the most appropriate treatment planning and delivery practice for lung SBRT. To gauge the range of practice, quantify its variability and identify where consensus might be achieved, ESTRO surveyed the medical physics community. MATERIALS AND METHODS: An online survey was distributed to ESTRO's physicist membership in 2022, covering experience, dose and fractionation, target delineation, dose calculation and planning practice, imaging protocols, and quality assurance. RESULTS: Two-hundred and forty-four unique answers were collected after data cleaning. Most respondents were from Europe the majority of which had more than 5 years' experience in SBRT. The large majority of respondents deliver lung SBRT with the VMAT technique on C-arm Linear Accelerators (Linacs) employing daily pre-treatment CBCT imaging. A broad spectrum of fractionation schemes were reported, alongside an equally wide range of dose prescription protocols. A clear preference was noted for prescribing to 95% or greater of the PTV. Several issues emerged regarding the dose calculation algorithm: 22% did not state it while 24% neglected to specify the conditions under which the dose was calculated. Contouring was usually performed on Maximum or Average Intensity Projection images while dose was mainly computed on the latter. No clear indications emerged for plan homogeneity, complexity, and conformity assessment. Approximately 40% of the responders participated in inter-centre credentialing of SBRT in the last five years. Substantial differences emerged between high and low experience centres, with the latter employing less accurate algorithms and older equipment. CONCLUSION: The survey revealed an evident heterogeneity in numerous aspects of the clinical implementation of lung SBRT treatments. International guidelines and codes of practice might promote harmonisation.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans.

PURPOSE: Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments. MATERIALS AND METHODS: A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm. RESULTS: The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans. CONCLUSION: A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation.

PURPOSE: Within a multi-institutional project, we aimed to assess the transferability of knowledge-based (KB) plan prediction models in the case of whole breast irradiation (WBI) for left-side breast irradiation with tangential fields (TF). METHODS: Eight institutions set KB models, following previously shared common criteria. Plan prediction performance was tested on 16 new patients (2 pts per centre) extracting dose-volume-histogram (DVH) prediction bands of heart, ipsilateral lung, contralateral lung and breast. The inter-institutional variability was quantified by the standard deviations (SDint) of predicted DVHs and mean-dose (Dmean). The transferability of models, for the heart and the ipsilateral lung, was evaluated by the range of geometric Principal Component (PC1) applicability of a model to test patients of the other 7 institutions. RESULTS: SDint of the DVH was 1.8 % and 1.6 % for the ipsilateral lung and the heart, respectively (20 %-80 % dose range); concerning Dmean, SDint was 0.9 Gy and 0.6 Gy for the ipsilateral lung and the heart, respectively (<0.2 Gy for contralateral organs). Mean predicted doses ranged between 4.3 and 5.9 Gy for the ipsilateral lung and 1.1-2.3 Gy for the heart. PC1 analysis suggested no relevant differences among models, except for one centre showing a systematic larger sparing of the heart, concomitant to a worse PTV coverage, due to high priority in sparing the left anterior descending coronary artery. CONCLUSIONS: Results showed high transferability among models and low inter-institutional variability of 2% for plan prediction. These findings encourage the building of benchmark models in the case of TF-WBI.

Dosiomic-based prediction of dysgeusia in head & neck cancer patients treated with radiotherapy.

PURPOSE: To investigate the potential of dosiomics in predicting radiotherapy-induced taste distortion (dysgeusia) in head & neck (H&N) cancer. METHODS: A cohort of 80 H&N cancer patients treated with radical or adjuvant radiotherapy and with a follow-up of at least 24 months was enrolled. Treatment information, as well as tobacco and alcohol consumption were also collected. The whole tongue was manually delineated on the planning CT and mapped to the dose map retrieved from the treatment planning system. For every patient, 6 regions of the tongue were examined; for each of them, 145 dosiomic features were extracted from the dose map and fed to a logistic regression model to predict the grade of dysgeusia at follow-up, with and without including clinical features. A mean dose-based model was considered for reference. RESULTS: Both dosiomics and mean dose models achieved good prediction performance for acute dysgeusia with AUC up to 0.88. For the dosiomic model, the central and anterior ⅔ regions of the tongue were the most predictive. For all models, a gradual reduction in the performance was observed at later times for chronic dysgeusia prediction, with higher values for dosiomics. The inclusion of smoke and alcohol habits did not improve model performances. CONCLUSION: The dosiomic analysis of the dose to the tongue identified features able to predict acute dysgeusia. Dosiomics resulted superior to the conventional mean dose-based model for chronic dysgeusia prediction. Larger, prospective studies are needed to support these results before integrating dosiomics in radiotherapy planning.

Patient specific quality assurance in SBRT: a systematic review of measurement-based methods.

This topical review focuses on Patient-Specific Quality Assurance (PSQA) approaches to stereotactic body radiation therapy (SBRT). SBRT requires stricter accuracy than standard radiation therapy due to the high dose per fraction and the limited number of fractions. The review considered various PSQA methods reported in 36 articles between 01/2010 and 07/2022 for SBRT treatment. In particular comparison among devices and devices designed for SBRT, sensitivity and resolution, verification methodology, gamma analysis were specifically considered. The review identified a list of essential data needed to reproduce the results in other clinics, highlighted the partial miss of data reported in scientific papers, and formulated recommendations for successful implementation of a PSQA protocol.

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.

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.

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.

Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields.

PURPOSE: To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI). MATERIALS AND METHODS: Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SDint of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model's geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes. RESULTS: The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R2 = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible. CONCLUSIONS: Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.

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.

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.

Technical Note: An IBEX adaption toward image biomarker standardization.

PURPOSE: Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open-source radiomic software, and we developed and validated standardized-IBEX (S-IBEX), an adaptation of IBEX to IBSI. METHODS: Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re-implemented wherever differences emerged: in particular, contour-to-binary-mask conversion, image sub-portion extraction, re-segmentation, gray-level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S-IBEX. On a patient phantom, S-IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. RESULTS: Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non-negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S-IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. CONCLUSIONS: The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S-IBEX, a free IBSI-compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.

Performance evaluation of a new time of flight PET/CT scanner: Results of a multicenter study.

PURPOSE: The aim of this multicenter study was to evaluate the performance of the upgraded version of the Ingenuity TF PET/CT scanner, according to the NEMA NU-2 2012 standards. METHODS: Spatial resolution, sensitivity, count rate response, scatter fraction, image quality and accuracy were evaluated on three Ingenuity TF scanners installed in Italian hospitals. Furthermore, energy and timing resolution were measured. A detailed image quality phantom analysis was performed to evaluate the effect of different clinical reconstruction parameters, including the application of PSF correction. RESULTS: Results show an average spatial resolution of 4.7 mm and an average absolute system sensitivity of 7.9 cps/kBq. The average maximum NECR was 119.83 kcps at 20.67 kBq/ml, while the maximum true event rate was 322.62 kcps at the concentration of 24.51 kBq/ml. The average maximum bias below NECR peak was 12.58%. All the results of NEMA tests were in agreement with the values declared by the manufacturer. The estimated average energy and timing resolution were 10.83% and 536.2 ps, respectively. Image quality phantom analysis obtained with different reconstruction settings showed that PSF correction was the parameter that affected mainly on contrast recovery coefficient, while the iteration number and amplitude of Gaussian filter had no significant effect. Of relevance, the application of PSF correction never led to recovery coefficient values higher than 100% and to Gibbs or edge artifacts. CONCLUSIONS: The new Ingenuity TF model shows physical performance similar to other scanners of the latest generation for all standard NEMA NU2-2012 measurements.

A dedicated cloud system for real-time upfront quality assurance in pediatric radiation therapy.

PURPOSE: Pediatric radiotherapy (RT) is a highly specialized field, requiring great experience to delineate correctly tumor targets and organs at risk. To reduce treatment failures related to planning inaccuracies and to obtain robust clinical results despite the limited numbers of enrolled pediatric patients, the SIOP PNET5MB clinical trial on medulloblastoma requires a real-time, pre-radiation review of the RT treatment (craniospinal irradiation and boost plan) under the direct responsibility of the national coordinator center. Here we describe the centralized radiotherapy quality assurance (QA) program developed in Italy for this purpose. METHODS: Using the software package VODCA (MSS, Hagendorn, Switzerland, www.vodca.ch ), we developed a cloud platform able to handle computed tomography (CT) images and RT objects and to support the complete workflow required by the review process in the context of the SIOP PNET5 trial. RESULTS: All Italian centers participating in the PNET5 trial adopted the proposed QA system. 24 patients were successfully enrolled and reviewed. For 15 patients (62.5%), one or more plan revisions were requested for the craniospinal irradiation plan and for 11 patients (45.8%) plan revisions were requested for the boost. RT was delivered after the plan was centrally approved for all enrolled patients. So far, in Italy, no patients have been excluded from PNET5 due to dosimetric incompliance to the protocol or for exceeding the RT starting time limit. CONCLUSION: The cloud platform successfully supported the trial workflow, producing official review documents. This efficient QA was crucial to guarantee optimized treatments and protocol compliance for all pediatric patients enrolled in the SIOP protocol.

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.

Efficiently train and validate a RapidPlan model through APQM scoring.

PURPOSE: The aim of this study was to propose and validate an intuitive method for training and to validate knowledge-based planning (KBP) systems based on a patient-specific plan quality scoring. METHODS: A sample of 80 clinical plans of prostate cancer patients were ranked on the basis of the Adjusted Plan Quality Metric (APQM%). This quality metric was computed normalizing the Plan Quality Metric (PQM%) score to the best possible OAR sparing estimated by the Feasibility DVH (FDVH) algorithm. Two different plan libraries were created, purging all the plans below the first quartile or below the median the APQM% distribution. These libraries were used to populate and train two RapidPlan models: respectively, the APMQ25% and the APMQ50% models. No further refinements or actions were undertaken on these two models. Their performances were benchmarked against another two RapidPlan models. An Uncleaned model, which was populated and trained with the initial sample of 80 plans, and a Cleaned model, obtained through the standard iterative cleaning and refinement process suggested by the vendor and in literature. The outcomes of a planning test based on 20 patients within the training library (closed loop) and 20 patients outside of the training library (open-loop) were compared through various DVH metrics and the PQM% score. RESULTS: The selection through APQM% thresholding roughly preserves the geometric variety of the Cleaned model; only the APMQ50% model showed a modest broadness reduction. The models generated through APQM% thresholding showed target coverage and OARs sparing equal or superior to the Uncleaned and Cleaned models both for the closed- and the open-loop tests. No significant differences were found between the four models. PQM% analysis ranked the overall plan quality as: 86.5 ± 6.5% APQM50% , 83.1 ± 5.9% APQM25% , 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the closed-loop test; 84.9 ± 7.6% APQM50% , 82.6 ± 7.9% APQM25% , 80.39 ± 10.6% Cleaned and 79.4 ± 8.5% Uncleaned in the open-loop test. CONCLUSIONS: Forward feeding a RapidPlan model through a thresholding selection based on APQM% is proven to produce equal or better results than a model based on a manually and iteratively refined population. A tighter APQM% threshold turns approximately into a higher average quality of plans generated with RapidPlan. A trade-off must be found between the mean quality of the KBP library and its numerosity. The proposed KBP feeding method helps the KBP user, because it makes the model refinement more intuitive and less time consuming.