ESTRO 2021 Abstract Book

S481

ESTRO 2021

OC-0617 Gastric deformation models for adaptive radiotherapy: Personalized vs Population-based strategy M. Bleeker 1 , M.C. Hulshof 1 , A. Bel 1 , J. Sonke 2 , A. van der Horst 1 1 Amsterdam UMC, University of Amsterdam, Radiation Oncology, Amsterdam, The Netherlands; 2 Netherlands Cancer Institute, Radiation Oncology, Amsterdam, The Netherlands Purpose or Objective An adaptive approach is likely beneficial for pre-operative gastric cancer radiotherapy due to large day-to-day stomach deformations. The aim is to compare a personalized and population-based strategy to predict stomach shape for subsequent definition of a library of plans. Materials and Methods 19 healthy volunteers underwent 3 successive MRI sessions (total timespan 2-3h), in which we acquired 3D mDIXON images in exhale breathhold with an empty (3h of fasting), half-full (half a meal) and full stomach (remaining half meal). Images of each volunteer were rigidly registered on bony anatomy. Stomachs were delineated and triangulated meshes were created (Fig 1). For both strategies (Fig 2), the non-rigid iterative closest point algorithm was used as deformable registration between meshes. For the personalized strategy, the empty stomach was mapped to the half-full stomach for each volunteer. This vector field was extrapolated to predict a full stomach of equal volume to the true full stomach. For the population-based strategy, deformation vectors were acquired between half-full and full stomach for each volunteer. Using rigid and then deformable registration, we acquired point-to-point correspondence between every half-full stomach and a reference stomach. Using this correspondence, deformation vectors were transferred to the corresponding points on the reference stomach, resulting in 19 deformation vectors to a full stomach for every point on the reference stomach. For each volunteer, a population-based full stomach was created by applying scaled average deformation vectors of the 18 other volunteers to the half-full stomach (using the point-to-point correspondence) to predict a full stomach of equal volume to the true full stomach. We evaluated strategy performance by comparing the following parameters between predicted and true full stomach (paired tests): Hausdorff distance (d H ), 75% of nearest neighbor distances from predicted to true stomach (d 75% ), Dice coefficient, missed volume (i.e., volume of true stomach outside predicted stomach + 10- mm uniform margin). Results For population-based vs personalized strategy, we found comparable d H (median[IQR], 17[13−21] vs 21[15−25] mm, p=0.06), smaller d 75% (5[4−6] vs 7[5−9] mm, p=0.003), larger Dice (0.87[0.85−0.89] vs 0.82[0.77−0.87], p=0.004), and less missed volume (0[0−1] vs 1[0−2] %, p=0.004). Visual assessment showed more shape similarities between half-full and full stomach than between empty and either half-full or full stomach. Conclusion The population-based strategy better predicted filled stomach shape. Empty stomach may be more susceptible to shape changes due to surrounding organs and intestinal filling, possibly affecting the prediction potential of our personalized strategy. It remains difficult to accurately predict stomach shape, due to its complicated deformations. A dose calculation study is needed to test whether shape predicting performance of our population-based model is sufficient in a library of plans strategy.