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Biol."],"published-print":{"date-parts":[[2025,5,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> To better integrate into processes like rapid adaptive planning and quality assurance, this study aims to propose a lightweight and universal proton spot dose calculation model suitable for arbitrary energies. <jats:italic>Approach.<\/jats:italic> Given the alignment between the characteristics of proton spot dose deposition and the sequence learning capabilities of the long short-term memory (LSTM) network, the lightweight model, multi-energy dose LSTM (MED-LSTM), is proposed. To comprehensively investigate the effectiveness of model, we trained and evaluated it on prostate, nasopharynx, and lung cases consistently. <jats:italic>Main results<\/jats:italic>. Regarding the results for spot dose, the prostate, nasopharynx, and lung models achieved average gamma passing rates of 99.93%, 99.81%, and 99.89% respectively under the (1%, 3 mm) criterion. Under the (1%, 1 mm) criterion, the rates were 99.06%, 97.18%, and 98.32%, respectively. For the intensity-modulated proton therapy plan dose, the prostate model achieved optimal performance with gamma passing rates of 99.88% and 98.52% under the (1%, 3 mm) and (1%, 1 mm) criteria, respectively. Following this, the lung model achieved rates of 99.22% and 93.41%. The nasopharynx model exhibited the poorest performance, with rates of 99.56% and 88.95%, respectively. It is evident that the MED-LSTM model demonstrates extremely high dose calculation accuracy in most cases. However, visible deviations occur in some spot samples for the nasopharynx and lung cases due to structural tissue differences. <jats:italic>Significance.<\/jats:italic> The MED-LSTM model can rapidly and accurately determine the proton spot dose at any energy with relatively low number of parameters. This exciting outcome holds broad prospects for applications and research directions.<\/jats:p>","DOI":"10.1088\/1361-6560\/add3b9","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T22:57:37Z","timestamp":1746226657000},"page":"105014","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Lightweight and universal deep learning model for fast proton spot dose calculation at arbitrary energies"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0622-9671","authenticated-orcid":true,"given":"Bo","family":"Pang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1752-3468","authenticated-orcid":true,"given":"Shuoyan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiling","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2806-1214","authenticated-orcid":true,"given":"Zhiyong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"pmbadd3b9bib1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/mp.13262","article-title":"A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning","volume":"46","author":"Chen","year":"2019","journal-title":"Med. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-11-13","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}