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The integration of real-time magnetic resonance imaging (MRI) guidance and proton therapy would potentially improve the proton dose steering capability by reducing daily uncertainties due to anatomical variations. The use of a fixed beamline coupled with an axial patient couch rotation would greatly simplify the proton delivery with MRI guidance. Nonetheless, it is mandatory to assure that the plan quality is not deteriorated by the anatomical deformations due to patient rotation. In this work, an in-house tool allowing for intra-fractional per-beam adaptation of intensity-modulated proton plans (BeamAdapt) was implemented through features available in RayStation. A set of three MRIs was acquired for two healthy volunteers (V1, V2): (1) no rotation/static, (2) rotation to the right and (3) left. V1 was rotated by 15°, to simulate a clinical pediatric abdominal case and V2 by 45°, to simulate an extreme patient rotation case. For each volunteer, a total of four intensity-modulated pencil beam scanning plans were optimized on the static MRI using virtual abdominal targets and two-three posterior-oblique beams. Beam angles were defined according to the angulations on the rotated MRIs. With BeamAdapt, each original plan was initially converted into separate plans with one beam per plan. In an iterative order, individual beam doses were non-rigidly deformed to the rotated anatomies and re-optimized accounting for the consequent deformations and the beam doses delivered so far. For evaluation, the final accumulated dose distribution was propagated back to the static MRI. Planned and adapted dose distributions were compared by computing relative differences between dose-volume histogram metrics. Absolute target dose differences were on average below 1% and organs-at-risk mean dose differences were below 3%. With BeamAdapt, not only intra-fractional per-beam proton plan adaptation coupled with axial patient rotation is possible but also the need for a rotating gantry during MRI guidance might be mitigated. [ABSTRACT FROM AUTHOR]

• Deep-learning networks for prediction of PBS and VMAT doses were established. • Relative errors between planned and predicted doses were minimal in the target. • Strong correlation (r > 0.9) was found between planned and predicted DVH metrics. • The networks classified the PBS dosimetric benefit over VMAT with 98% precision. • Proposed networks can accurately predict the treatment modality selection outcome. Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5 mm/ 3%) and volumetric-modulated arc therapy (VMAT) plans (5 mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation. The networks' performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔD mean = D VMAT -D PBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔD mean. Average DVH differences between planned and predicted dose distributions were ≤ |6%| for both modalities. The networks classified the organs-at-risk D mean difference as a gain (ΔD mean > 0) with 98% precision. An identical modality selection based on planned compared to predicted ΔD mean was made for 18/20 patients. Deep learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualisation might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable. [ABSTRACT FROM AUTHOR]

Purpose/Objective: Flank irradiation for Wilms' tumor (WT) is currently performed at our institute using a cone-beam computed tomography-guided volumetric modulated arc (VMATCBCT) workflow. By adding real-time magnetic resonance imaging (MRI) guidance to the treatment, safety margins could be reduced. The study purpose was to quantify the potential reduction of the planning target volume (PTV) margin and its dosimetric impact when using an MRI-guided intensity modulated radiation therapy (IMRTMRI) workflow compared to the VMATCBCT workflow. Material/Methods: 4D-CT, MRI and CBCT scans acquired during preparation and treatment of 15 patients, were used to estimate both geometric, motion and patient set-up systematic (∑) and random (σ) errors for VMATCBCT and IMRTMRI workflows. The mean PTV (PTVmean) expansion was calculated using the van Herk formula. Treatment plans were generated using five margin scenarios (PTVmean ± 0, 1 and 2 mm). Furthermore, the IMRTMRI plans were optimized with a 1.5T transverse magnetic field turned-on to realistically model an MRI-guided treatment. Plans were evaluated using dose-volume statistics (p<.01, Wilcoxon). Results: Analysis of ∑ and σ errors resulted in a PTVmean of 5 mm for the VMATCBCT and 3 mm for the IMRTMRI workflows in each orthogonal direction. Target coverage was unaffected by the margin decrease with a mean V95%=100% for all margin scenarios. For the PTVmean, an average reduction of the mean dose to the organs at risk (OARs) was achieved with IMRTMRI compared to VMATCBCT: 3.4 ± 2.4% (p<.01) for the kidney, 3.4 ± 2.1% (p<.01) for the liver, 2.8 ± 3.0% (p<.01) for the spleen and 4.9 ± 3.8% (p<.01) for the pancreas, respectively. Conclusions: Imaging data in children with WT demonstrated that the PTV margin could be reduced isotropically down to 2 mm when using the IMRTMRI compared to the VMATCBCT workflow. The former results in a dose reduction to the OARs while maintaining target coverage. [ABSTRACT FROM AUTHOR]

Introduction: Decreasing the radiation dose in the surgical area is important to lower the risk of wound complications when surgery and radiotherapy are combined for the treatment of spinal metastases. The purpose of this study was to compare the radiation dose in the surgical area for spinal metastases between single fraction external beam radiotherapy (EBRT), single fraction stereotactic body radiotherapy (SBRT) and single fraction SBRT with active sparing (SBRT-AS) of the posterior surgical area. Methods: Radiotherapy treatment plans for EBRT, SBRT and SBRT-AS of the posterior surgical area were created for 13 patients with spinal metastases. A single fraction of 8Gy was prescribed to the spinal metastasis in the EBRT plan. For the SBRT treatment plans, a single fraction of 18Gy was prescribed to the metastasis and 8Gy to the rest of the vertebral body. For the SBRT plan with active sparing the dose in the designated surgical area was minimized without compromising the dose to the organs at risk. Results: The median dose in the surgical area was 2.6Gy (1.6-5.3Gy) in the SBRT plan with active sparing of the surgical area compared to a median dose of 3.7Gy (1.6-6.3Gy) in the SBRT plan without sparing and 6.5Gy (3.5-9.1Gy) in the EBRT plans (p < .001). The radiation doses to the spinal metastases and organs at risk were not significantly different between the SBRT plan with and without sparing the surgical area. Conclusions: The radiation dose to the surgical area is significantly decreased with the use of SBRT compared to EBRT. Active sparing of the surgical area further decreased the mean radiation dose in the surgical area without compromising the dose to the spinal metastasis and the organs at risk. [ABSTRACT FROM AUTHOR]

Background and purpose A methodology is presented to quantify the uncertainty associated with linear accelerator-based frameless intracranial stereotactic radiotherapy (SRT) combining end-to-end phantom tests and clinical data. Methods and materials The following steps of the SRT chain were analysed: planning computed tomography (CT) and magnetic resonance (MR) scans registration, target volume delineation, CT and cone beam CT (CBCT) registration and intrafraction-patient displacement. The overall accuracy was established with an end-to-end test. The measured uncertainties were combined, deriving the total systematic ( Σ T ) and random ( σ T ) error components, to estimate the GTV-PTV margin. Results The uncertainty in the MR-CT registration was on average 0.40 mm (averaged over AP, CC and LR directions). Rotational variations were smaller than 0.5° in all directions. Interobser variation in GTV delineation was on average 0.29 mm. The uncertainty in the CBCT-CT registration was on average 0.15 mm. Again, rotational variations were smaller than 0.5° in all directions. The systematic and random intrafraction displacement errors were on average 0.55 mm and 0.45 mm, respectively. The systematic and random positional errors from the end-to-end test were on average 0.49 mm and 0.53 mm, respectively. Combining these uncertainties resulted in an average Σ T = 0.9 mm and σ T = 0.7 mm and an average GTV-PTV margin of 2.8 mm. Conclusion This comprehensive methodology including end-to-end tests enabled a GTV-PTV margin calculation considering all sources of uncertainties. This generic method can also be used for other treatment sites. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET) is a promising tool for monitoring the three-dimensional dose distribution in charged particle radiotherapy. PET imaging during or shortly after proton treatment is based on the detection of annihilation photons following the ß+-decay of radionuclides resulting from nuclear reactions in the irradiated tissue. Therapy monitoring is achieved by comparing the measured spatial distribution of irradiation-induced ß+-activity with the predicted distribution based on the treatment plan. The accuracy of the calculated distribution depends on the correctness of the computational models, implemented in the employed Monte Carlo (MC) codes that describe the interactions of the charged particle beam with matter and the production of radionuclides and secondary particles. However, no well-established theoretical models exist for predicting the nuclear interactions and so phenomenological models are typically used based on parameters derived from experimental data. Unfortunately, the experimental data presently available are insufficient to validate such phenomenological hadronic interaction models. Hence, a comparison among the models used by the different MC packages is desirable. In this work, starting from a common geometry, we compare the performances of MCNPX, GATE and PHITS MC codes in predicting the amount and spatial distribution of proton-induced activity, at therapeutic energies, to the already experimentally validated PET modelling based on the FLUKA MC code. In particular, we show how the amount of ß+-emitters produced in tissue-like media depends on the physics model and cross-sectional data used to describe the proton nuclear interactions, thus calling for future experimental campaigns aiming at supporting improvements of MC modelling for clinical application of PET monitoring. [ABSTRACT FROM AUTHOR]