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Med. Biol."],"published-print":{"date-parts":[[2026,1,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    During radiotherapy, the radiation dose delivered to circulating blood can result in radiation-induced lymphopenia, which is correlated with adverse clinical outcomes like lower survival. Increasingly complex models to simulate radiation dose delivery to circulating blood have been developed in response, and their inclusion during radiotherapy treatment planning has been suggested. However, performing full dynamic blood dose simulations which take into account temporal considerations such as blood flow dynamics and treatment delivery time during the iterative treatment planning process is currently infeasible. This work presents a quasi-instantaneous deep learning-based approach to estimate blood dose simulation results to allow for their inclusion during treatment planning.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We used treatment planning computed tomography images and dose-volume histograms of 157 head-and-neck cancer patients to perform dynamic blood dose simulations (HEDOS). Subsequently, a deep neural network composed of fully-connected layers and a Transformer encoder was trained to estimate the blood dose distribution obtained from HEDOS, using the same inputs as HEDOS. We used 126 patients\u2019 data for training and internal validation and the remaining 31 patients\u2019 data for testing. To evaluate the proposed method, we calculated the Kullback\u2013Leibler (KL) divergence between the prediction results and the ground truth data. Additionally, we compared the minimum dose delivered to 90% of the blood particles receiving the highest dose (D\n                    <jats:sub>90%<\/jats:sub>\n                    ) to estimate the model\u2019s clinical efficacy.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The average and standard deviation of KL divergence between the prediction and the ground truth were 0.099 and 0.092, respectively. The D\n                    <jats:sub>90%<\/jats:sub>\n                    calculated from the predicted distribution showed a mean-absolute-percentage error of 4.60% compared to the ground truth.\n                    <jats:italic>Significance.<\/jats:italic>\n                    A deep learning-based model capable of accurately and quasi-instantaneously predicting the results of dynamic blood dose simulations was developed, paving the way for the inclusion of dynamic blood dose simulations during radiotherapy treatment planning.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae3047","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T22:52:51Z","timestamp":1766443971000},"page":"015024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based prediction of dynamic blood dose estimates for head-and-neck cancer"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1165-1509","authenticated-orcid":true,"given":"Hoyeon","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9854-0011","authenticated-orcid":true,"given":"Sebastian","family":"Tattenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"pmbae3047bib1","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1016\/j.ijrobp.2003.12.024","type":"journal-article","article-title":"Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT\/linear accelerator system","volume":"59","author":"Barker","year":"2004","journal-title":"Int. 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