Validation of Fully Automated Robust Multicriterial Treatment Planning for Head and Neck Cancer IMPT

'iCycle-pBAO': Automated patient-specific beam-angle selection in proton therapy applied to oropharyngeal cancer

OBJECTIVE

This study aimed to develop a fully-automated patient tailored beam-angle optimisation approach for intensity-modulated proton therapy (IMPT). For oropharynx cancer patients, the dosimetric impact of increasing the number of fields from 4 to 12 was systematically assessed.

APPROACH

A total-beam-space heuristic was developed to simultaneously select optimal patient specific candidate beam directions, according to a cost-function that penalises dose to OARs involved in clinically used NTCPs. The method was dosimetrically validated by comparisons with fixed 4- and 6-field clinical beam-angle templates and equiangular configurations, including 72-field equiangular. The latter served as dosimetric 'Utopia' benchmark for the other evaluated beam configurations.

MAIN RESULT

Using 4 optimised patient-specific fields instead of the clinical 4-field beam-angle template resulted in (xerostomia NTCP + dysphagia NTCP)-reductions for all patients, with averages of 3.0 %-point (range: 1.1-5.8) for grade 2 toxicity and 1.2 %-point (range: 0.3-2.8) for grade 3. For 6 fields these reductions were 2.4 %-point (range: 0.0-5.0) and 0.8 %-point (range: -0.1-2.1). Xerostomia NTCPs significantly reduced with increasing numbers of patient-specific fields with a levelling off at 10-12 fields with NTCP values that closely approached those for Utopia 72-field equiangular plans. Beam angle optimisation took 52 min.

CONCLUSION

Automated, patient-tailored beam-angle optimisation could enhance IMPT plans at acceptable optimisation times. Improvements compared to the clinical beam-angle templates were highly patient-specific.

Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck

Objective. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined scenarios, albeit at a price of increased planning times. We investigated a deep learning (DL) strategy for dose predictions in individual error scenarios in head and neck cancer IMPT treatment planning, enabling direct evaluation of plan robustness. The model is able to differentiate between scenarios by using embeddings of the scenario index.Approach. To accommodate resolution disparities in planning CT-scans and accommodate the setup error scenarios, we introduced scenario-specific isocentric distance maps as inputs to the DL models. For 392 H&N cancer patients, high-quality 9-scenario ground truth (GT) robust plans were generated with wish-list driven fully automated multi-criteria optimization. The scenario index is converted to one-hot-vector that is used to derive the scenarios embeddings through the training of the DL model, aiding the model to predict a scenario specific dose distribution.Main results. The model achieved within 1%-point of agreement with the GT the predictedV95%of the voxelwise minimum dose for CTV Low and CTV High for 96% and 75% respectively of the test patients. Considering all robustness scenarios, median differences were 0.035%-point for CTV HighV95%, 0.11%-point for CTV LowV95%, 0.29 GyE for parotidsDmean, 0.7 GyE for submandibular glandsDmeanand 0.9 GyE for oral cavityDmean. Prediction of full 3D dose distributions for all scenarios took around 14 s.Significance. Predicting individual scenarios for robust proton therapy using DL dose prediction is feasible, enabling direct robustness evaluation of the predicted scenario doses.

Reducing the lateral dose penumbra in IMPT by incorporating transmission pencil beams

OBJECTIVE

In intensity-modulated proton therapy (IMPT), Bragg peaks result in steep distal dose fall-offs, while the lateral IMPT dose fall-off is often less steep than in photon therapy. High-energy pristine transmission ('shoot through') pencil beams have no Bragg peak in the patient, but show a sharp lateral penumbra at the target level. We investigated whether combining Bragg peaks with Transmission pencil beams ('IMPT&TPB') could improve head-and-neck plans by exploiting the steep lateral dose fall-off of transmission pencil beams.

APPROACH

Our system for automated multi-criteria IMPT plan optimisation was extended for combined optimisation of BPs and TPBs. The system generates for each patient a Pareto-optimal plan using a generic 'wish-list' with prioritised planning objectives and hard constraints. For eight nasopharynx cancer patients (NPC) and eight oropharynx cancer (OPC) patients, the IMPT&TPB plan was compared to the competing conventional IMPT plan with only Bragg peaks, which was generated with the same optimiser, but without transmission pencil beams.

MAIN RESULTS

Clinical OAR and target constraints were met in all plans. By allowing transmission pencil beams in the optimisation, on average 14 of the 25 investigated OAR plan parameters significantly improved for NPC, and 9 of the 17 for OPC, while only one OPC parameter showed small but significant deterioration. Non-significant differences were found in the remaining parameters. In NPC, cochlea Dmean reduced by up to 17.5 Gy and optic nerve D2% by up to 11.1 Gy.

CONCLUSION

Compared to IMPT, IMPT&TPB resulted in comparable target coverage with overall superior OAR sparing, the latter originating from steeper dose fall-offs close to OARs.

A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTBre-planning) schedule, including extensive robustness analyses.Approach. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTBre-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations.Main results. For 14/67 repeat-CTs, offlineTBre-planning resulted in <50% probability ofD98%≥ 95% of the prescribed dose (Dpres) in one or both CTVs, which never happened with online re-optimization. With offlineTBre-planning, eight repeat-CTs had zero probability of obtainingD98%≥ 95%Dpresfor CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p< 10-5for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average.Significance. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

SISS-MCO: large scale sparsity-induced spot selection for fast and fully-automated robust multi-criteria optimisation of proton plans

Objective.Intensity modulated proton therapy (IMPT) is an emerging treatment modality for cancer. However, treatment planning for IMPT is labour-intensive and time-consuming. We have developed a novel approach for multi-criteria optimisation (MCO) of robust IMPT plans (SISS-MCO) that is fully automated and fast, and we compare it for head and neck, cervix, and prostate tumours to a previously published method for automated robust MCO (IPBR-MCO, van de Water 2013).Approach.In both auto-planning approaches, the applied automated MCO of spot weights was performed with wish-list driven prioritised optimisation (Breedveld 2012). In SISS-MCO, spot weight MCO was applied once for every patient after sparsity-induced spot selection (SISS) for pre-selection of the most relevant spots from a large input set of candidate spots. IPBR-MCO had several iterations of spot re-sampling, each followed by MCO of the weights of the current spots.Main results.Compared to the published IPBR-MCO, the novel SISS-MCO resulted in similar or slightly superior plan quality. Optimisation times were reduced by a factor of 6 i.e. from 287 to 47 min. Numbers of spots and energy layers in the final plans were similar.Significance.The novel SISS-MCO automatically generated high-quality robust IMPT plans. Compared to a published algorithm for automated robust IMPT planning, optimisation times were reduced on average by a factor of 6. Moreover, SISS-MCO is a large scale approach; this enables optimisation of more complex wish-lists, and novel research opportunities in proton therapy.