{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:50:50Z","timestamp":1791946250056,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81803047"],"award-info":[{"award-number":["81803047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2019kfyXKJC061"],"award-info":[{"award-number":["2019kfyXKJC061"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Compared to the pencil\u2010beam algorithm, the Monte\u2010Carlo (MC) algorithm is more accurate for dose calculation but time\u2010consuming 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\u2010modulated proton therapy (IMPT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A novel recurrent U\u2010net (RU\u2010net) 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\u2010net with a five\u2010fold cross\u2010validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak\u2010monitor unit (MU) model, this work develops the multi\u2010MU 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\u2010volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared to the peak\u2010MU model, the multi\u2010MU model led to smaller mean absolute errors (3.03% vs. 2.05%,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.005), higher gamma\u2010passing rate (2 mm, 3%: 97.42% vs. 93.69%,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D\n                      <jats:sub>98%<\/jats:sub>\n                      : 3.03% vs. 6.08%,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.017; in Bladder V30: 3.08% vs. 5.28%,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.028; and in Bladder V20: 3.02% vs. 4.42%,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.017). Considering more prior knowledge, the multi\u2010MU model had better\u2010predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the multi\u2010MU model is 0.46%, with a dropout rate of 10%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This method was a nearly real\u2010time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB)\u00a0analytical algorithms used in prostate cancer. This RU\u2010net might be used in plan robustness optimization and robustness evaluation in the future.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.15702","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T04:35:58Z","timestamp":1652243758000},"page":"5451-5463","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Feasibility study of fast intensity\u2010modulated proton therapy dose prediction method using deep neural networks for prostate cancer"],"prefix":"10.1002","volume":"49","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering Huazhong University of Science and Technology  Wuhan 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