Physics and Imaging in Radiation Oncology · Published 2026-08-01 · DOI 10.1016/j.phro.2026.101052
Cornel Zachiu, Gijsbert H. Bol, Alexis N.T.J. Kotte, Thomas Willigenburg, Matteo Maspero, Mark H.F. Savenije, Johannes C.J. de Boer, Jochem R.N. van der Voort van Zyp, Cornelis A.T. van den Berg, Bas W. Raaymakers
Background and purpose: Daily auto-contouring remains a workflow bottleneck in magnetic resonance-guided adaptive prostate radiotherapy (MRgRT). This study proposes and clinically validates a novel deep learning-enhanced deformable image registration (DIR) solution to accelerate this critical step. Materials and Methods: A hybrid framework combining a 3D nnU-Net segmenting bladder/rectum on planning/daily MRI with an in-house DIR algorithm was implemented for 5-fraction prostate MRgRT (5×7.25 Gy) on an MR-Linac. The DIR uses nnU-Net contours to propagate target and organs-of-interest structures. The solution was clinically deployed and evaluated in 275 patients/1375 fractions. Results:: Evaluation following clinical introduction, has shown a median contouring time of ≈190 s, halving the time required by the previously-employed vendor-provided solution. Quantitative evaluation showed high agreement with clinically approved contours: Dice similarity coefficients >0.9 and 95th percentile Hausdorff distances <2.0 mm for most structures. Conclusions:: The implemented solution demonstrated reliable, high-accuracy daily auto-contouring, significantly accelerating MRgRT workflows. It has become our institutional standard for prostate treatments. Future work will extend this approach to additional treatment sites and modalities.
Abstract from DOAJ. Public domain (CC0 1.0).
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Zachiu, C., Bol, G., Kotte, A., et al. (2026). Deep learning-enhanced image registration for accelerating daily adaptive magnetic resonance imaging-guided prostate radiotherapy. Physics and Imaging in Radiation Oncology. https://doi.org/10.1016/j.phro.2026.101052