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• First study of seminal vesicle intrafraction motion during MR-Linac RT sessions. • High quality 3D cine-MRI and seminal vesicle tracking is feasible during RT beam-on. • Seminal vesicles show larger intrafraction rotational spread than prostate. • 5 mm isometric expansion provides seminal vesicle coverage probability of 99%. To evaluate seminal vesicle (SV) intrafraction motion using cinematic magnetic resonance imaging (cine-MR) during the delivery of online adaptive MR-Linac radiotherapy fractions, in preparation of MR-guided extremely hypofractionated radiotherapy for intermediate to high-risk prostate cancer patients. Fifty prostate cancer patients were treated with 5 × 7.25 Gy on a 1.5 Tesla MR-Linac. 3D Cine-MR imaging was started simultaneously and acquired over the full beam-on period. Intrafraction motion in this cine-MR was determined for each SV separately with a previously validated soft-tissue contrast-based tracking algorithm. Motion statistics and coverage probability for the SVs and prostate were determined based on the obtained results. SV motion was automatically determined during the beam-on period (approx. 10 min) for 247 fractions. SV intrafraction motion shows larger spread than prostate intrafraction motion and increases over time. This difference is especially evident in the anterior and cranial translation directions. Significant difference in rotation about the left–right axis was found, with larger rotation for the SVs than the prostate. Intra-fraction coverage probability of 99% can be achieved when using 5 mm isometric expansion for the left and right SV and 3 mm for the prostate. This is the first study to investigate SV intrafraction motion during MR-guided RT sessions on an MR-Linac. We have shown that high quality 3D cine-MR imaging and SV tracking during RT is feasible with beam-on. The tracking method as described may be used as input for a fast replanning algorithm, which allows for intrafraction plan adaptation. [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]

To evaluate prostate intrafraction motion using MRI during the full course of online adaptive MR-Linac radiotherapy (RT) fractions, in preparation of MR-guided extremely hypofractionated RT. Five low and intermediate risk prostate cancer patients were treated with 20 × 3.1 Gy fractions on a 1.5T MR-Linac. Each fraction, initial MRI (Pre) scans were obtained at the start of every treatment session. Pre-treatment planning MRI contours were propagated and adapted to this Pre scan after which plan re-optimization was started in the treatment planning system followed by dose delivery. 3D Cine-MR imaging was started simultaneously with beam-on and acquired over the full beam-on period. Prostate intrafraction motion in this cine-MR was determined with a previously validated soft-tissue contrast based tracking algorithm. In addition, absolute accuracy of the method was determined using a 4D phantom. Prostate motion was completely automatically determined over the full on-couch period (approx. 45 min) with no identified mis-registrations. The translation 95% confidence intervals are within clinically applied margins of 5 mm, and plan adaption for intrafraction motion was required in only 4 out of 100 fractions. This is the first study to investigate prostate intrafraction motions during entire MR-guided RT sessions on an MR-Linac. We have shown that high quality 3D cine-MR imaging and prostate tracking during RT is feasible with beam-on. The clinically applied margins of 5 mm have proven to be sufficient for these treatments and may potentially be further reduced using intrafraction plan adaptation guided by cine-MR imaging. [ABSTRACT FROM AUTHOR]

Poster: RTT track: Treatment planning and dose calculation / QC and QA PO-1868: In silico hypofractionation feasibility study for lymph node oligometastases SBRT on the MRI-linac D. Winkel, W.S.C. Eppinga, M.P.W. Intven, A.M. Werensteijn-Honingh, J. Hes, L.M.W. Snoeren, G.H. Bol, B.W. Raaymakers, I.M. Jürgenliemk-Schulz, P.S. Kroon. [Extracted from the article]

Teaching Lecture: Dosimetry and QA for MR-Linacs SP-0727: Dosimetry and QA for MR-Linacs B. Van Asselen, S.L. Hackett, S.J. Woodings, B.W. Raaymakers, J.W.H. Wolthaus. [Extracted from the article]

Poster discussion: RTT 2 PD-0672: Effect of a vacuum mattress on bone & target intra-fraction motion during MR-guided lymph node SBRT A.M. Werensteijn-honingh, P.S. Kroon, D. Winkel, C.G. Gadellaa-Van Hooijdonk, G.G. Sikkes, N.G.P.M. Vissers, W.S.C. Eppinga, M. Intven, B.W. Raaymakers, I.M. Jürgenliemk-Schulz. [Extracted from the article]

Poster discussion: PH: Adaptive radiotherapy and interfraction motion management 2 PD-0557: Biomechanical quality assurance criteria for deformable registration in image guided radiotherapy C. Zachiu, B. Denis de Senneville, M. Ries, B.W. Raaymakers. [Extracted from the article]

Proffered Papers: Proffered papers 18: Intra-fractional motion management OC-0341: Seminal vesicle motion tracking in 3D cine-MR during MR-Linac prostate treatments D. De Muinck Keizer, T. Willigenburg, M.D. Den Hartogh, J.R.N. Van der Voort van Zyp, B.W. Raaymakers, J.J.W. Lagendijk, H.C.J. De Boer. [Extracted from the article]

Poster Highlights: Poster highlights 17 CL: Palliation PH-0529: Prostate intrafraction motion during the full course of MR-guided radiotherapy sessions D. De Muinck Keizer, L.G.W. Kerkmeijer, T. Willigenburg, M.D. Den Hartogh, J.R.N. Van der Voort van Zyp, E.N. De Groot-van Breugel, B.W. Raaymakers, J.J.W. Lagendijk, H.C.J. De Boer. [Extracted from the article]