Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases - Huang, Peng; Shang, Jiawen; Hu, Zhihui; Liu, Zhiqiang; Yan, Hui (Frontiers in Oncology, 2024)
Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time.Accurate prediction of its dose distribution can provide a general standard in evaluating the quality of treatment plan. In this study, a deep learning model is applied to dose prediction of single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases.: A U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. Then 3D dose distribution is then predicted with the input of CT image and contours of region of interests (ROIs). Total 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs) including (D max and D mean ) for OARs, (D 98 , D 95 , D 50 and D 2 ) for PTVs, homogeneity index and conformity index. The similarity between the predicted and clinically approved plan dose distribution are also evaluated.Result: For 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%.The mean absolute differences of D 98 , D 95 , D 50 and D 2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0% and 3.0%, respectively. The prediction accuracy of OARs for D max and D mean are within 3.2% and 1.2%. The averaged DSC is from 0.86 to 1 for all tested patients.U-ResNet is viable to produce accurate dose distribution which is comparable to those of the clinically approved treatment plans. The predicted 40 results can be used in improving current treatment planning design, plan quality, and efficiency, etc.