Deep learning–based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers

Abstract

BackgroundIn recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.PurposeTo develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.MethodsA total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions.ResultsThe predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of and were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions.ConclusionsDeep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases

“…In addition, other types of networks, such as Resnet ( 27 , 29 , 30 ) and GAN ( 31 – 33 ), are also used for dose prediction. So far, the deep U-net-like architecture and its variants with various types of residual or dense blocks become the mainstream structure for dose prediction ( 34 – 38 ).…”

Deep learning based linear energy transfer calculation for proton therapy

“…For both tasks, a multitude of DL models have been utilized, yielding notable accuracy in their outcomes. Examples of knowledge-based dose prediction tasks encompass various UNet-like architectures (Nguyen et al 2019, Ahn et al 2021, Liu et al 2021, Gronberg et al 2023, Generative Adversarial Network (GAN) (Kearney et al 2020, Zhan et al 2022, and diffusion models (Feng et al 2023, Fu et al 2023, Zhang et al 2023. Despite variances in model performance and inference speed, these models for dose prediction typically rely on patient's anatomy and physician's contours as input and do not use beam-specific information.…”