{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:40:00Z","timestamp":1792514400890,"version":"4.5.0"},"reference-count":73,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Carbon ion radiotherapy (CIRT) offers superior dose conformity and biological effectiveness; however, its precision is highly sensitive to respiratory motion, leading to dose uncertainty and interplay effects that compromise treatment accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A comprehensive literature synthesis was conducted on studies employing motion\u2010resolved imaging techniques (e.g. SMEIR), dynamic dose prediction, 4D robust planning, and hybrid adaptive methods for CIRT. The study emphasised the use of Geant4 Monte Carlo simulations combined with deep learning models, including physics\u2010informed neural networks, to simulate and predict dose perturbations caused by respiratory motion. This review also examined clinical treatment planning systems, radiobiological modelling, and adaptive strategies to mitigate the effects of motion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The integration of Geant4 with deep learning enabled high\u2010fidelity, computationally efficient dose prediction under respiratory motion, improving accuracy in dose localization and adaptation. Motion management techniques such as respiratory gating, rescanning, and 4D robust optimization demonstrated enhanced tumor control and normal tissue sparing. Hybrid models combining Monte Carlo data and AI inference show promise for near\u2010real\u2010time adaptive therapy. Despite these advances, challenges remain, including limited motion\u2010rich datasets, computational demands for real\u2010time recalculations, and the complexity of incorporating variable relative biological effectiveness (RBE) into adaptive planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Combining Geant4\u2010based Monte Carlo simulations with deep learning frameworks shows emerging promise for controlling carbon ion dose distributions amid respiratory motion, though direct clinical validation for this combination remains limited. These hybrid models may enhance the precision and adaptability of CIRT, offering improved outcomes. Future efforts should focus on clinical validation, standardization, and overcoming computational and data limitations to facilitate clinical translation of motion\u2010adaptive CIRT. These approaches remain at Technology Readiness Levels (TRL 3\u20135) and require prospective clinical validation before routine implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70766","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:45:13Z","timestamp":1792511113000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic control of carbon\u2010ion radiation dose distribution amid respiratory motion challenges: A review of prediction research using geant4 and deep learning physics\u2010constrained models"],"prefix":"10.1002","volume":"27","author":[{"given":"Jianhua","family":"Ding","sequence":"first","affiliation":[{"name":"School of Medicine Faculty of Health and Medical Sciences Taylor's University  Subang Jaya Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing &amp; Technology Asia Pacific University of Technology &amp; Innovation  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chai Hong","family":"Yeong","sequence":"additional","affiliation":[{"name":"School of Medicine Faculty of Health and Medical Sciences Taylor's University  Subang Jaya Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2009.183"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/jjco\/hys104"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2017.11.004"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470\u20102045(14)70412\u20107"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2006.09.7816"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/57\/11\/R99"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/58\/15\/R131"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20150275"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.806153"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15470"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1399589"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2020.05.003"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ac36e7"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.12.017"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aba1a3"},{"key":"e_1_2_12_17_1","unstructured":"FreisledererP 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