Gastric deformation models for adaptive radiotherapy: Personalized vs population-based strategy

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Background and purpose: To create a library of plans (LoP) for gastric cancer adaptive radiotherapy, accurate predictions of shape changes due to filling variations are essential. The ability of two strategies (personalized and population-based) to predict stomach shape based on filling was evaluated for volunteer and patient data to explore the potential for use in a LoP. Materials and methods: For 19 healthy volunteers, stomachs were delineated on MRIs with empty (ES), half-full (HFS) and full stomach (FS). For the personalized strategy, a deformation vector field from HFS to corresponding ES was acquired and extrapolated to predict FS. For the population-based strategy, the average deformation vectors from HFS to FS of 18 volunteers were applied to the HFS of the remaining volunteer to predict FS (leave-one-out principle); thus, predictions were made for each volunteer. Reversed processes were performed to predict ES. To validate, for seven gastric cancer patients, the volunteer population-based model was applied to their pre-treatment CT to predict stomach shape on 2–3 repeat CTs. For all predictions, volume was made equal to true stomach volume. Results: FS predictions were satisfactory, with median Dice similarity coefficient (mDSC) of 0.91 (population-based) and 0.89 (personalized). ES predictions were poorer: mDSC = 0.82 for population-based; personalized strategy yielded unachievable volumes. Population-based shape predictions (both ES and FS) were comparable between patients (mDSC = 0.87) and volunteers (0.88). Conclusion: The population-based model outperformed the personalized model and demonstrated its ability in predicting filling-dependent stomach shape changes and, therefore, its potential for use in a gastric cancer LoP.
Original languageEnglish
Pages (from-to)126-132
Number of pages7
JournalRadiotherapy and oncology
Publication statusPublished - 1 Jan 2022


  • Adaptive radiotherapy
  • Deformation model
  • Gastric cancer
  • Library of plans
  • Shape prediction

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