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Med. Biol."],"published-print":{"date-parts":[[2025,4,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> To evaluate the impact of beam mask implementation and data aggregation on artificial intelligence-based dose prediction accuracy in proton therapy, with a focus on scenarios involving limited or highly heterogeneous datasets. <jats:italic>Approach.<\/jats:italic> In this study, 541 prostate and 632 head and neck (H&amp;N) proton therapy plans were used to train and evaluate convolutional neural networks designed for the task of dose prediction. Datasets were grouped by anatomical site and beam configuration to assess the impact of beam masks\u2014graphical depictions of radiation paths\u2014as a model input. We also evaluated the effect of combining datasets. Model performance was measured using dose-volume histograms (DVHs) scores, mean absolute error, mean absolute percent error, dice similarity coefficients (DSCs), and gamma passing rates. <jats:italic>Main results.<\/jats:italic> DSC analysis revealed that the inclusion of beam masks improved dose prediction accuracy, particularly in low-dose regions and for datasets with diverse beam configurations. Data aggregation alone produced mixed results, with improvements in high-dose regions but potential degradation in low-dose areas. Notably, combining beam masks and data aggregation yielded the best overall performance, effectively leveraging the strengths of both strategies. Additionally, the magnitude of the improvements was larger for datasets with greater heterogeneity, with the combined approach increasing the DSC score by as much as 0.2 for a subgroup of H&amp;N cases characterized by small size and heterogeneity in beam arrangement. DVH scores reflected these benefits, showing statistically significant improvements (<jats:italic>p<\/jats:italic> &lt; 0.05) for the more heterogeneous H&amp;N datasets. <jats:italic>Significance.<\/jats:italic> Artificial intelligence-based dose prediction models incorporating beam masks and data aggregation significantly improve accuracy in proton therapy planning, especially for complex cases. This technique could accelerate the planning process, enabling more efficient and effective cancer treatment strategies.<\/jats:p>","DOI":"10.1088\/1361-6560\/adc236","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T22:58:21Z","timestamp":1742338701000},"page":"075016","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Deep learning techniques for proton dose prediction across multiple anatomical sites and variable beam configurations"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-2856","authenticated-orcid":true,"given":"Ivan","family":"Vazquez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danfu","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramon M","family":"Salazar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary P","family":"Gronberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Sjogreen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7808-3024","authenticated-orcid":true,"given":"Tyler D","family":"Williamson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"X Ronald","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas J","family":"Whitaker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven J","family":"Frank","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurence E","family":"Court","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9122-0401","authenticated-orcid":true,"given":"Ming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"pmbadc236bib1","doi-asserted-by":"publisher","first-page":"154","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":"Radiat. 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