Gastric deformation models for adaptive radiotherapy: Personalized vs population-based strategy.
Radiother Oncol
; 166: 126-132, 2022 01.
Article
em En
| MEDLINE
| ID: mdl-34861269
ABSTRACT
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 ANDMETHODS:
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Gástricas
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Radioterapia de Intensidade Modulada
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Radioterapia Guiada por Imagem
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article