{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:25:06Z","timestamp":1773001506142,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"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":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for improved treatment response without an increase in toxicities, commonly utilized for palliation of bulky tumors. The LRT treatment planning process is complex, while eligible patients often have an urgent need for expedited treatment start. In this study, we aimed to develop a simulation\u2010free workflow for volumetric modulated arc therapy (VMAT)\u2010based LRT planning via deep learning\u2010predicted synthetic CT (sCT) to expedite treatment initiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Two deep learning models were initially trained using 3D U\u2010Net architecture to generate sCT from diagnostic CTs (dCT) of the thoracic and abdomen regions using a training dataset of 50 patients. The models were then tested on an independent dataset of 15 patients using image similarity analysis assessing\n                      <jats:italic>mean absolute error<\/jats:italic>\n                      (MAE) and\n                      <jats:italic>structural similarity index measure<\/jats:italic>\n                      (SSIM) as metrics. VMAT\u2010based LRT plans were generated based on sCT and recalculated on the planning CT (pCT) for dosimetric accuracy comparison. Differences in dose volume histogram (DVH) metrics between pCT and sCT plans were assessed using the Wilcoxon signed\u2010rank test.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The final sCT prediction model demonstrated high image similarity to pCT, with a MAE and SSIM of 38.93\u00a0\u00b1\u00a014.79 Hounsfield Units (HU) and 0.92\u00a0\u00b1\u00a00.05 for the thoracic region, and 73.60\u00a0\u00b1\u00a022.90 HU and 0.90\u00a0\u00b1\u00a00.03 for the abdominal region, respectively. There were no statistically significant differences between sCT and pCT plans in terms of organ\u2010at\u2010risk and target volume DVH parameters, including maximum dose (Dmax), mean dose (Dmean), dose delivered to 90% (D90%) and 50% (D50%) of target volume, except for minimum dose (Dmin) and (D10%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>With demonstrated high image similarity and adequate dose agreement between sCT and pCT, our study is a proof\u2010of\u2010concept for using deep learning predicted sCT for a simulation\u2010free treatment planning workflow for VMAT\u2010based LRT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70137","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T10:40:17Z","timestamp":1749724817000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Simulation\u2010free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study"],"prefix":"10.1002","volume":"26","author":[{"given":"Libing","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan Y.","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Safia K.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan B.","family":"Ashman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego Santos","family":"Toesca","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael P.","family":"Grams","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher L.","family":"Deufel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-9127","authenticated-orcid":false,"given":"Jingwei","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Alabama at Birmingham  Birmingham Alabama USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2620-1893","authenticated-orcid":false,"given":"Yi","family":"Rong","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Phoenix Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2021.100866"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2020.100639"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2023.08.061"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2023.101401"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1667\/RADE-20-00047.1"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.03.030"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13566-012-0064-5"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v11i3.3163"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0360-3016(95)00114-3"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1520-6823(1996)4:1<41::AID-ROI7>3.0.CO;2-M"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.01.073"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctro.2019.10.004"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.14338\/IJPT-22-00028"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.11.023"},{"key":"e_1_2_11_16_1","unstructured":"WrightJ.Proton pencil beam scanning GRID for treatment of bulky tumors (ProGRID). 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