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.

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.

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.

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.