Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions’ models for prostate cancer

BackgroundThe aim of this study was to clarify factors predicting the performance of knowledge-based planning (KBP) models in volume modulated arc therapy for prostate cancer in terms of sparing the organ at risk (OAR).Materials and methodsIn three institutions, each KBP model was trained by more than 20 library plans (LP) per model. To validate the characterization of each KBP model, 45 validation plans (VP) were calculated by the KBP system. The ratios of overlap between the OAR volume and the planning target volume (PTV) to the whole organ volume (V_overlap/V_whole) were analyzed for each LP and VP. Regression lines between dose–volume parameters (V₉₀, V₇₅, and V₅₀) and V_overlap/V_whole were evaluated. The mean OAR dose, V₉₀, V₇₅, and V₅₀ of LP did not necessarily match those of VP.ResultsIn both the rectum and bladder, the dose–volume parameters for VP were strongly correlated with V_overlap/V_whole at institutes A, B, and C (R > 0.74, 0.85, and 0.56, respectively). Except in the rectum at institute B, the slopes of the regression lines for LP corresponded to those for VP. For dose–volume parameters for the rectum, the ratios of slopes of the regression lines in VP to those in LP ranged 0.51–1.26. In the bladder, most ratios were less than 1.0 (mean: 0.77).ConclusionFor each OAR, each model made distinct dosimetric characterizations in terms of V_overlap/V_whole. The relationship between dose–volume parameters and V_overlap/V_whole of OARs in LP predicts the KBP models’ performance sparing OARs.     

Assessment of a conventional volumetric-modulated arc therapy knowledge-based planning model applied to the new Halcyon© O-ring linac in locoregional breast cancer radiotherapy

IntroductionThe aim of this study was to evaluate the performance of a knowledge-based planning (KBP) model for breast cancer trained on plans performed on a conventional linac with 6 MV FF (flattening filter) beams and volumetric-modulated arc therapy (VMAT) for plans performed on the new jawless Halcyon© system with 6 MV FFF (flattening filter-free) beams.Materials and methodsBased on the RapidPlan© (RP) KBP optimization engine, a DVH Estimation Model was first trained using 56 VMAT left-sided breast cancer treatment plans performed on a conventional linac, and validated on another 20 similar cases (without manual intervention). To determine the capacity of the model for Halcyon©, an additional cohort of 20 left-sided breast cancer plans was generated with RP and analyzed for both TrueBeam© and Halcyon© machines. Plan qualities between manual vs RP (followed by manual intervention) Halcyon© plans set were compared qualitatively by blinded review by radiation oncologists for 10 new independent plans.ResultsHalcyon© plans generated with the VMAT model trained with conventional linac plans showed comparable target dose distribution compared to TrueBeam© plans. Organ sparing was comparable between the 2 devices with a slight decrease in heart dose for Halcyon© plans. Nine out of ten automatically generated Halcyon© plans were preferentially chosen by the radiation oncologists over the manually generated Halcyon© plans.ConclusionA VMAT KBP model driven by plans performed on a conventional linac with 6 MV FF beams provides high quality plans performed with 6 MV FFF beams on the new Halcyon© linac.     

Proton or photon radiosurgery for cardiac ablation of ventricular tachycardia? Breath and ECG gated robust optimization

PurposeVentricular tachycardia (VT) is a life-threatening heart disorder. The aim of this preliminary study is to assess the feasibility of stereotactic body radiation therapy (SBRT) photon and proton therapy (PT) plans for the treatment of VT, adopting robust optimization technique for both irradiation techniques.MethodsECG gated CT images (in breath hold) were acquired for one patient. Conventional planning target volume (PTV) and robust optimized plans (25GyE in single fraction) were simulated for both photon (IMRT, 5 and 9 beams) and proton (SFO, 2 beams) plans. Robust optimized plans were obtained both for protons and photons considering in the optimization setup errors (5 mm in the three orthogonal directions), range (±3.5%) and the clinical target volume (CTV) motion due to heartbeat and breath-hold variability.ResultsThe photon robust optimization method, compared to PTV-based plans, showed a reduction in the average dose to the heart by about 25%; robust optimization allowed also reducing the mean dose to the left lung from 3.4. to 2.8 Gy for 9-beams configuration and from 4.1 to 2.9 Gy for 5-beams configuration. Robust optimization with protons, allowed further reducing the OAR doses: average dose to the heart and to the left lung decreased from 7.3 Gy to 5.2 GyE and from 2.9 Gy to 2.2 GyE, respectively.ConclusionsOur study demonstrates the importance of the optimization technique adopted in the treatment planning system for VT treatment. It has been shown that robust optimization can significantly reduce the dose to healthy cardiac tissues and that PT further increases this gain.     

Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning.Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans.ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D_2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D_98% and D_2%, rectum V_40Gy and V_60Gy, bladder D_2% and V_60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43–1.08) vs. 0.91 (−5.98–6.25).ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.     

Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study

BackgroundThe aim of this study was to investigate the performance of the RapidPlan (RP ) using models registered pseudostructures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer.Materials and methodsPseudo-structures around the PTV were retrospectively contoured for patients who had completed treatment at five institutions. For 22 common patients, plans were generated with a single optimization for models with two (RP_2), four (RP_4), and five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations.ResultsMost dosimetric parameters showed no major differences between each RP model. In particular, the rectum D_max, V_50Gy, and V_40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. The average proportions of plans achieving acceptable criteria for dosimetric parameters were close to 100% for all models. Using RP_2, the average time for the VMAT planning was reduced by 88 minutes compared with the clinical plan.ConclusionThe RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time.     

Advanced optimization methods for whole pelvic and local prostate external beam therapy

PurposeRadiation treatment planning inherently involves multiple conflicting planning goals, which makes it a suitable application for multicriteria optimization (MCO). This study investigates a MCO algorithm for VMAT planning (VMAT–MCO) for prostate cancer treatments including pelvic lymph nodes and uses standard inverse VMAT optimization (sVMAT) and Tomotherapy planning as benchmarks.MethodsFor each of ten prostate cancer patients, a two stage plan was generated, consisting of a stage 1 plan delivering 22 Gy to the prostate, and a stage 2 plan delivering 50.4 Gy to the lymph nodes and 56 Gy to the prostate with a simultaneous integrated boost. The single plans were generated by three planning techniques (VMAT–MCO, sVMAT, Tomotherapy) and subsequently compared with respect to plan quality and planning time efficiency.ResultsPlan quality was similar for all techniques, but sVMAT showed slightly better rectum (on average D_mean −7%) and bowel sparing (D_mean −17%) compared to VMAT–MCO in the whole pelvic treatments. Tomotherapy plans exhibited higher bladder dose (D_mean +42%) in stage 1 and lower rectum dose (D_mean −6%) in stage 2 than VMAT–MCO. Compared to manual planning, the planning time with MCO was reduced up to 12 and 38 min for stage 1 and 2 plans, respectively.ConclusionMCO can generate highly conformal prostate VMAT plans with minimal workload in the settings of prostate-only treatments and prostate plus lymph nodes irradiation. In the whole pelvic plan manual VMAT optimization led to slightly improved OAR sparing over VMAT–MCO, whereas for the primary prostate treatment plan quality was equal.     

Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning

IntroductionPrevious literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique.Materials and MethodsA support vector machine (SVM) was developed – using a database of 500 previous VMAT plans – to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed.ResultsPredicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum).ConclusionThis study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.     

The robustness of dual isocenter VMAT radiation therapy for bilateral lymph node positive breast cancer

PurposeTo investigate the use of dual isocenters for VMAT planning in patients with lymph node positive synchronous bilateral breast cancer (BBC) compared to a single isocenter option.MethodsTreatment plans of 11 patients with lymph node positive BBC were retrospectively analyzed using two different VMAT planning techniques: dual-isocenter split-arc VMAT plans (Iso2) were compared with mono-isocenter VMAT plans (Iso1). For Iso2 plans, PTV dose was investigated after introducing ±2 and ±5 mm couch shift errors between the two isocenters in the lateral, longitudinal and vertical direction.ResultsFor both techniques the planning aims for PTV coverage and OARs were met. The mean dose for the bilateral lungs and heart was reduced from 11.3 Gy and 3.8 Gy to 10.9 Gy (p < .05) and 3.6 Gy (p < .05), respectively, for Iso2 plans when compared to Iso1 plans. Positive statistically significant correlation (rho = 0.76, p = .006) was found between PTV volume and D2cc_PTV for Iso1 plans. No clinically significant change was seen in the D98_CTV or D2cc_PTV after the 2 and 5 mm errors were introduced between isocenters for Iso2 plans.ConclusionsThe split arc method was shown to be a feasible treatment technique in the case of synchronous BBC for both mono and dual isocenter techniques. The dose parameters were slightly favoring dual-isocenter option instead of mono-isocenter. The dual-isocenter method was shown to be a robust treatment option in the presence of ≤5 mm errors in the shifts between the two isocenters.     

Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms

In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle’s location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle’s location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy – the crossover and mutation operator hybrid approach – is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6 MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human planner intervention. A comparison of the results with the optimized solution obtained using a similar optimization model but with human planner intervention revealed that the proposed algorithm produced optimized plans superior to that developed using the manual plan. The proposed algorithm can generate admissible solutions within reasonable computational times and can be used to develop fully automated IMRT treatment planning methods, thus reducing human planners’ workloads during iterative processes.     

Virtual Tangential-fields Arc Therapy (ViTAT) for whole breast irradiation: Technique optimization and validation

PurposeTo test the performances of a volumetric arc technique named ViTAT (Virtual Tangential-fields Arc Therapy) mimicking tangential field irradiation for whole breast radiotherapy.MethodsViTAT plans consisted in 4 arcs whose starting/ending position were established based on gantry angle distribution of clinical plans for right and left-breast. The arcs were completely blocked excluding the first and last 20°. Different virtual bolus densities and thicknesses were preliminarily evaluated to obtain the best plan performances. For 40 patients with tumor laterality equally divided between right and left sides, ViTAT plans were optimized considering the clinical DVHs for OARs (resulting from tangential field manual planning) to constrain them: ViTAT plans were compared with the clinical tangential-fields in terms of DVH parameters for both PTV and OARs.ResultsDistal angle values were suggested in the ranges [220°,240°] for the right-breast and [115°,135°] for the left-breast cases; medial angles were [60°,40°] for the right side and [295°,315°] for the left side, limiting the risk of collision. The optimal virtual bolus had −500 HU density and 1.5 cm thickness. ViTAT plans generated dose distributions very similar to the tangential-field plans, with significantly improved PTV homogeneity. The mean doses of ipsilateral OARs were comparable between the two techniques with minor increase of the low-dose spread in the range 2–15 Gy (few % volume); contralateral OARs were slightly better spared with ViTAT.ConclusionViTAT dose distributions were similar to tangential-fields. ViTAT should allow automatic plan optimization by developing knowledge-based DVH prediction models of patients treated with tangential-fields.     

Evaluation of clinical benefits achievable by using different optimization algorithms during real-time prostate brachytherapy

Background and purposeHigh dose rate (HDR) brachytherapy is a clinically used procedure in prostate cancer treatment. The purpose of this study was to present the influence of using different optimization algorithms in 3D-CBRT planning on the treatment plan quality.Materials and methodsTreatment plans were calculated for 15 patients – three plans for each patient using: geometrical optimization (GO), inverse optimization (IO) and blind inverse optimization (BIO). For each patient, PTV and OAR volumes, number of needles and geometry of the implant were set equal. Differences between dose distributions were tracked using: D90, V100, V200, Dmax (for prostate); D10, Dmax (for urethra); D10, V100, Dmax (for rectum).ResultsThe analysis of mean values of D90 and V100 in the prostate showed that inverse algorithms gave the best results (mean D90 was 12.1% for BIO and 9.3% for IO better than for GO, mean V100 was 8.2% for BIO and 6.3% for IO better than for GO). From a clinical point of view, GO diminished the doses in the PTV and urethra in all analyzed parameters. The lowest mean doses in the rectum were achieved for plans optimized with IO and BIO (mean D10: 61.2% for GO, 58.1% for IO, 58.0% for BIO; mean Dmax: 92.8% for GO, 85.1% for IO, 83.6% for BIO).ConclusionsApplication of the blind inverse optimization (BIO) algorithm led to clinically best dose parameters for PTV and the rectum. Use of geometrical optimization (GO) led to smaller doses in the urethra, which was however associated with a certain dose decrease also in PTV.     

Novel knowledge-based treatment planning model for hypofractionated radiotherapy of prostate cancer patients

PurposeTo demonstrate the strength of an innovative knowledge-based model-building method for radiotherapy planning using hypofractionated, multi-target prostate patients.Material and methodsAn initial RapidPlan model was trained using 48 patients who received 60 Gy to prostate (PTV60) and 44 Gy to pelvic nodes (PTV44) in 20 fractions. To improve the model's goodness-of-fit, an intermediate model was generated using the dose-volume histograms of best-spared organs-at-risk (OARs) of the initial model. Using the intermediate model and manual tweaking, all 48 cases were re-planned. The final model, trained using these re-plans, was validated on 50 additional patients. The validated final model was used to determine any planning advantage of using three arcs instead of two on 16 VMAT cases and tested on 25 additional cases to determine efficacy for single-PTV (PTV60-only) treatment planning.ResultsFor model validation, PTV V_95% of 99.9% was obtained by both clinical and knowledge-based planning. D_1% was lower for model plans: by 1.23 Gy (PTV60, CI = [1.00, 1.45]), and by 2.44 Gy (PTV44, CI = [1.72, 3.16]). OAR sparing was superior for knowledge-based planning: ΔD_mean = 3.70 Gy (bladder, CI = [2.83, 4.57]), and 3.22 Gy (rectum, CI = [2.48, 3.95]); ΔD_2% = 1.17 Gy (bowel bag, CI = [0.64, 1.69]), and 4.78 Gy (femoral heads, CI = [3.90, 5.66]). Using three arcs instead of two, improvements in OAR sparing and PTV coverage were statistically significant, but of magnitudes < 1 Gy. The model failed at reliable DVH predictions for single PTV plans.ConclusionsOur knowledge-based model delivers efficient, consistent plans with excellent PTV coverage and improved OAR sparing compared to clinical plans.     

Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML)

PurposeTo predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML).MethodsA data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment.ResultsSuccessful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99.ConclusionsMachine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.     

Automatic VMAT technique to treat glioblastoma: A two years’ experience

PurposeThe present work aims to guide the physicist in order to start automated planning for the VMAT treatment of glioblastoma multiforme (GBM) by giving a recipe that was set up and tested during a long-term (two years) evaluation.MethodsAn automatic technique in AutoPlanning module of the Pinnacle3 (Philips Medical Systems, Fitchburg, WI) treatment planning system was created and validated by comparing dose distributions of automatic plans (APs) and manual plans (MPs) and by performing a blind AP-MP comparison on a cohort of 20 patients. Automatic technique was then applied to 145 patients and failures were recorded i.e. the number of times for which dose distributions produced by the automatic module were not suitable for treatment.ResultsEach of the 20 APs considered in the validation step was clinically acceptable and proved to be better (15 cases) or equal (5 cases) respect to MPs. A statistically significant improvement in brain stem, optic pathways, cochleae, pituitary gland and scalp sparing was observed for APs, while no statistically significant differences were recorded in target coverage or plan parameters. For only 5 cases out of the 145 plans the operator intervention was needed in order to obtain a clinical acceptable plan, while for the remaining 140 plans the automatic created solution was suitable.ConclusionsA straightforward automatic procedure has been created and tested in our clinic. The AutoPlanning technique proposed represents a reliable tool to improve treatment planning efficiency and the recipe, here presented, could be simply imported to every radiotherapy center.     

The influence of Acuros XB on dose volume histogram metrics and tumour control probability modelling in locally advanced non-small cell lung cancer

PurposeRadiation therapy plans are assessed using dose volume metrics derived from clinical toxicity and outcome data. In this study, plans for patients with locally advanced non-small cell lung cancer (LA-NSCLC) are examined in the context of the implementation of the Acuros XB (AXB) dose calculation algorithm focussing on the impact on common metrics. Methods: Volumetric modulated arc therapy (VMAT) plans were generated for twenty patients, using the Analytical Anisotropic Algorithm (AAA) and recalculated with AXB for both dose to water (D_w) and dose to medium (D_m). Standard dose volume histogram (DVH) metrics for both targets and organs-at-risk (OARs) were extracted, in addition to tumour control probability (TCP) for targets. Results: Mean dose to the planning target volume (PTV) was not clinically different between the algorithms (within ±1.1 Gy) but differences were seen in the minimum dose, D_99% and D_98% as well as for conformity and homogeneity metrics. A difference in TCP was seen for AXB_Dm plans versus both AXB_Dw and AAA plans. No clinically relevant differences were seen in the lung metrics. For point doses to spinal cord and oesophagus, the AXB_Dm values were lower than AXB_Dw, by up to 1.0 Gy. Conclusion: Normalisation of plans to the mean/median dose to the target does not need to be adjusted when moving from AAA to AXB. OAR point doses may decrease by up to 1 Gy with AXB_Dm, which can be accounted for in clinical planning. Other OAR metrics do not need to be adjusted.     

Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction

PurposeTreatment plans manually generated in clinical routine may suffer from variations and inconsistencies in quality. Using such plans for validating a DVH prediction algorithm might obscure its intrinsic prediction accuracy. In this study we used a recently published large database of Pareto-optimal prostate cancer plans to assess the prediction accuracy of a commercial knowledge-based DVH prediction algorithm, RapidPlan. The database plans were consistently generated with automated planning using an independent optimizer, and can be considered as aground truth of plan quality.MethodsPrediction models were generated using training sets with 20, 30, 45, 55 and 114 Pareto-optimal plans. Model-20 and Model-30 were built using 5 groups of randomly selected training patients. For 60 independent Pareto-optimal validation plans, predicted and database DVHs were compared.ResultsFor model-114, differences between predicted and database mean doses of more than ± 10% in rectum, anus and bladder, occurred for 23.3%, 55.0%, and 6.7% of the validation plans, respectively. For rectum V_65Gy and V_75Gy, differences outside the ±10% range were observed in 21.7% and 70.0% of validation plans, respectively. For 61.7% of validation plans, inaccuracies in predicted rectum DVHs resulted in a deviation in predicted NTCP for rectal bleeding outside ±10%. With smaller training sets the DVH prediction performance deteriorated, showing dependence on the selected training patients.ConclusionEven when analysed with Pareto-optimal plans with highly consistent quality, clinically relevant deviations in DVH predictions were observed. Such deviations could potentially result in suboptimal plans for new patients. Further research on DVH prediction models is warranted.     

Knowledge-based automatic optimization of adaptive early-regression-guided VMAT for rectal cancer

PurposeTo implement a knowledge-based (KB) optimization strategy to our adaptive (ART) early-regression guided boosting technique in neo-adjuvant radio-chemotherapy for rectal cancer.Material and methodsThe protocol consists of a first phase delivering 27.6 Gy to tumor/lymph-nodes (2.3 Gy/fr-PTV₁), followed by the ART phase concomitantly delivering 18.6 Gy (3.1 Gy/fr) and 13.8 Gy (2.3 Gy/fr) to the residual tumor (PTV_ART) and to PTV₁ respectively. PTV_ART is obtained by expanding the residual GTV, as visible on MRI at fraction 9. Forty plans were used to generate a KB-model for the first phase using the RapidPlan tool. Instead of building a new model, a robust strategy scaling the KB-model to the ART phase was applied. Both internal and external validation were performed for both phases: all automatic plans (RP) were compared in terms of OARs/PTVs parameters against the original plans (RA).ResultsThe resulting automatic plans were generally better than or equivalent to clinical plans. Of note, V_30Gy and V_40Gy were significantly improved in RP plans for bladder and bowel; gEUD analysis showed improvement for KB-modality for all OARs, up to 3 Gy for the bowel.ConclusionsThe KB-model generated for the first phase was robust and it was also efficiently adapted to the ART phase. The performance of automatically generated plans were slightly better than the corresponding manual plans for both phases.     

Evaluation of fully automated a priori MCO treatment planning in VMAT for head-and-neck cancer

PurposeAutomated planning techniques aim to reduce manual planning time and inter-operator variability without compromising the plan quality which is particularly challenging for head-and-neck (HN) cancer radiotherapy. The objective of this study was to evaluate the performance of an a priori-multicriteria plan optimization algorithm on a cohort of HN patients.MethodsA total of 14 nasopharyngeal carcinoma (upper-HN) and 14 “middle-lower indications” (lower-HN) previously treated in our institution were enrolled in this study. Automatically generated plans (autoVMAT) were compared to manual VMAT or Helical Tomotherapy planning (manVMAT-HT) by assessing differences in dose delivered to targets and organs at risk (OARs), calculating plan quality indexes (PQIs) and performing blinded comparisons by clinicians. Quality control of the plans and measurements of the delivery times were also performed.ResultsFor the 14 lower-HN patients, with equivalent planning target volume (PTV) dosimetric criteria and dose homogeneity, significant decrease in the mean doses to the oral cavity, esophagus, trachea and larynx were observed for autoVMAT compared to manVMAT-HT. Regarding the 14 upper-HN cases, the PTV coverage was generally significantly superior for autoVMAT which was also confirmed with higher calculated PQIs on PTVs for 13 out of 14 patients, whereas PQIs calculated on OARs were generally equivalent. Number of MUs and total delivery time were significantly higher for autoVMAT compared to manVMAT. All plans were considered clinically acceptable by clinicians.ConclusionsOverall superiority of autoVMAT compared to manVMAT-HT plans was demonstrated for HN cancer. The obtained plans were operator-independent and required no post-optimization or manual intervention.