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However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG\u201043) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This paper aims to develop an end\u2010to\u2010end deep convolutional neural network\u2010based dose engine (DCNN\u2010DE) to achieve fast and accurate dose calculation for PGC SIBT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A DCNN model was trained using the patient's CT images and TG\u201043\u2010based dose maps as inputs, with the corresponding MCS\u2010based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I\u2010125 SIBT patient cases, and its transferability was tested on an additional 16 non\u2010PGC head and neck cancers (HNCs) I\u2010125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>On the PGC testing dataset, the DCNN\u2010DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67%\u00a0\u00b1\u00a00.47% for clinical target volume (CTV) <jats:italic>D<\/jats:italic><jats:sub>90<\/jats:sub> and 1.04%\u00a0\u00b1\u00a01.33% for skin <jats:italic>D<\/jats:italic><jats:sub>0.1cc<\/jats:sub>. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (<jats:italic>p\u00a0<\/jats:italic>&lt;\u00a00.001) and 3.1% (<jats:italic>p\u00a0<\/jats:italic>&lt;\u00a00.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non\u2010PGC HNC dataset, the DCNN\u2010DE exhibited good transferability, achieving a CTV <jats:italic>D<\/jats:italic><jats:sub>90<\/jats:sub> PAE of 1.88%\u00a0\u00b1\u00a01.73%. The DCNN\u2010DE can generate a dose map in less than 10 ms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We have developed and validated an end\u2010to\u2010end DCNN\u2010DE for PGC SIBT. The proposed DCNN\u2010DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17123","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T14:49:21Z","timestamp":1715870961000},"page":"6365-6377","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An end\u2010to\u2010end deep convolutional neural network\u2010based dose engine for parotid gland cancer seed implant brachytherapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Tianyu","family":"Xiong","sequence":"first","affiliation":[{"name":"Image Processing Center Beihang University  Beijing People's Republic of China"},{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Hong Kong SAR People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University  Hong Kong SAR People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fugen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Image Processing Center Beihang University  Beijing People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Image Processing Center Beihang University  Beijing People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery Peking University School of Stomatology  Beijing People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuwen","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University Medical Center  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00405-011-1663-2"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-09-0638"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-013-0313-6"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjoms.2014.09.016"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2016.07.005"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.597458"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2736790"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1646040"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.11.025"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2015.12.354"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16760"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.596360"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4747264"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/47\/3\/301"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3478278"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/13\/4987"},{"key":"e_1_2_8_18_1","first-page":"1","article-title":"Fast Monte Carlo\u2010based inverse planning for prostate brachytherapy by using deep learning","author":"Villa M","year":"2021","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104755"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/accd42"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.04.045"},{"key":"e_1_2_8_22_1","unstructured":"OktayO SchlemperJ FolgocLL et\u00a0al.Attention U\u2010Net: learning where to look for the pancreas.2018;abs\/1804.03999."},{"key":"e_1_2_8_23_1","doi-asserted-by":"crossref","unstructured":"ChenY LiuJ ZhangX QiX JiaJ LargeKernel3D: scaling up Kernels in 3D sparse CNNs. 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