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

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. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

In order to create a library of plans for gastric cancer adaptive radiotherapy, accurate predictions of stomach shape changes due to filling variations are crucial. This study compared personalized and population-based strategies for predicting stomach shape based on filling, and found that the population-based model outperformed the personalized model, demonstrating its potential for use in a gastric cancer treatment plan library.