Feasibility study of fast intensity‐modulated proton therapy dose prediction method using deep neural networks for prostate cancer. Issue 8 (19th May 2022)

• Record Type: Journal Article
• Authors: Wang, Wei; Chang, Yu; Liu, Yilin; Liang, Zhikai; Liao, Yicheng; Qin, Bin; Liu, Xu; Yang, Zhiyong
• Is Part Of: Medical physics. Volume 49:Issue 8(2022)
• Page Start: 5451
• Page End: 5463
• Publication Date: 2022-05-19
• Subjects: dose prediction -- deep learning -- intensity‐modulated proton therapy -- prostate cancer; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15702
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Compared to the pencil‐beam algorithm, the Monte‐Carlo (MC) algorithm is more accurate for dose calculation but time‐consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity‐modulated proton therapy (IMPT). Methods: A novel recurrent U‐net (RU‐net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU‐net with a five‐fold cross‐validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak‐monitor unit (MU) model, this work develops the multi‐MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and Monte Carlo (MC) dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose‐volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model. Results: Compared to the peak‐MU model, the multi‐MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma‐passing rate (2 mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the multi‐MU model had better‐predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the multi‐MU model is 0.46%, with a dropout rate of 10%. Conclusion: This method was a nearly real‐time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB) analytical algorithms used in prostate cancer. This RU‐net might be used in plan robustness optimization and robustness evaluation in the future.