Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy.
Clin Oncol (R Coll Radiol)
; 36(7): e209-e223, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38631974
ABSTRACT
AIMS:
Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. MATERIALS ANDMETHODS:
We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics.RESULTS:
The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%.CONCLUSION:
AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiotherapy Dosage
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Radiotherapy Planning, Computer-Assisted
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Uterine Cervical Neoplasms
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Radiotherapy, Intensity-Modulated
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Deep Learning
Limits:
Adult
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Aged
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Female
/
Humans
/
Middle aged
Language:
En
Journal:
Clin Oncol (R Coll Radiol)
Journal subject:
NEOPLASIAS
Year:
2024
Document type:
Article