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Biol."],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LET\n                    <jats:sub>d<\/jats:sub>\n                    ) using patient anatomy and dose-to-water (D\n                    <jats:sub>W<\/jats:sub>\n                    ) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.\n                    <jats:italic>Approach<\/jats:italic>\n                    . 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LET\n                    <jats:sub>d<\/jats:sub>\n                    distributions from CT images and D\n                    <jats:sub>W<\/jats:sub>\n                    . The accuracy of the LET\n                    <jats:sub>d<\/jats:sub>\n                    of the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The proposed model accurately inferred LET\n                    <jats:sub>d<\/jats:sub>\n                    distributions for each proton field in the test dataset. A single-field LET\n                    <jats:sub>d<\/jats:sub>\n                    calculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 \u00b1 0.14 MeV cm\n                    <jats:sup>\u22121<\/jats:sup>\n                    and a gamma passing rate of 97.4% \u00b1 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This study demonstrates that deep-learning-based models can efficiently calculate LET\n                    <jats:sub>d<\/jats:sub>\n                    with high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model\u2019s performance and evaluating its adaptability to different clinical scenarios.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad4844","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T18:29:58Z","timestamp":1715106598000},"page":"115058","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning based linear energy transfer calculation for proton therapy"],"prefix":"10.1088","volume":"69","author":[{"given":"Xueyan","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hok","family":"Wan Chan Tseung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas","family":"Moseley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexei","family":"Zverovitch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cian O","family":"Hughes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon","family":"George","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jedediah E","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William G","family":"Breen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5276-4158","authenticated-orcid":true,"given":"Jing","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"pmbad4844bib1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-021-01864-9","article-title":"Deep learning method for prediction of patient-specific dose distribution in breast cancer","volume":"16","author":"Ahn","year":"2021","journal-title":"Radiother. 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