{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T10:06:49Z","timestamp":1779790009120,"version":"3.53.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["24K10873"],"award-info":[{"award-number":["24K10873"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,26]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate reference dose distributions as a guideline tool for treatment planners and enabling virtual PBT dose assessment at institutions lacking PBT facilities to support clinical decision-making. This study proposes a dose gradient-aware DL training approach and a beam arrangement-free framework that predicts dose distributions from computed tomography (CT) images and target\/organs-at-risk structures without requiring beam arrangement inputs. Using data from 172 HCC patients, we systematically compared 20 DL models combining five architectures with four loss functions, including a novel dose gradient-aware loss capturing PBT-specific dose distribution characteristics arising from the Bragg peak. Prediction accuracy was evaluated using dose metrics for clinical target volume, planning target volume, and normal liver, 10% dose region volume assessment, visual examination, Dice similarity coefficient (DSC) for 10% dose region, and dose\u2013volume histogram (DVH) analysis. DL models employing dose gradient-aware loss demonstrated reduced prediction errors in target volume dose metrics, achieved the lowest errors in 10% dose region volume when combined with U-Net (MAE: 35.4\u00a0cm3, RMSE: 51.3\u00a0cm3), attained the highest DSC (0.88\u2009\u00b1\u20090.05), showed the closest DVH agreement with clinical plans for both target structures and normal liver. A comprehensive evaluation consistently confirmed the superiority of dose gradient-aware loss across all metrics, demonstrating the importance of physics-informed, dose gradient-aware learning for accurate PBT dose distribution prediction. Particularly, DL models combining simple architectures with dose gradient-aware loss predicted doses that closely approximated clinical doses in PBT.<\/jats:p>","DOI":"10.1093\/jrr\/rrag026","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:23:14Z","timestamp":1774268594000},"page":"369-383","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions"],"prefix":"10.1093","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4909-4423","authenticated-orcid":false,"given":"Shuta","family":"Ogawa","sequence":"first","affiliation":[{"name":"Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center , 7-172 Yatsuyamada, Koriyama, Fukushima, 963-8052 ,","place":["Japan"]},{"name":"Department of Radiation Oncology, Tohoku University Graduate School of Medicine , 1-1 Seiryou-machi, Aoba-ku, Sendai, Miyagi, 980-8574 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-3800","authenticated-orcid":false,"given":"Noriyuki","family":"Kadoya","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Tohoku University Graduate School of Medicine , 1-1 Seiryou-machi, Aoba-ku, Sendai, Miyagi, 980-8574 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8343-8037","authenticated-orcid":false,"given":"Takahiro","family":"Kato","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University , 10-6 Sakaemachi, Fukushima, Fukushima, 960-8516 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0104-9696","authenticated-orcid":false,"given":"Ryohei","family":"Kato","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center , 7-172 Yatsuyamada, Koriyama, Fukushima, 963-8052 ,","place":["Japan"]},{"name":"Department of Radiation Oncology, Tohoku University Graduate School of Medicine , 1-1 Seiryou-machi, Aoba-ku, Sendai, Miyagi, 980-8574 ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Tozuka","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Tohoku University Graduate School of Medicine , 1-1 Seiryou-machi, Aoba-ku, Sendai, Miyagi, 980-8574 ,","place":["Japan"]},{"name":"Department of Therapeutic Radiology, University of Yamanashi , 1110 Shimokato, Chuo-city, Yamanashi, 409-3898 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