{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T15:00:31Z","timestamp":1777906831817,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100016623","name":"Radiation Oncology Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016623","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is particularly impactful in spinal palliative care, where timely treatment is often important for symptom control and prevention of neurological deterioration. Although diagnostic CT (dCT) is frequently available earlier in the workflow, it can lead to geometric and dosimetric inaccuracies when used directly for treatment planning due to discrepancies in patient positioning, vertebral alignment, and table curvature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate an AI\u2010based method that transforms dCT into a simulation\u2010equivalent planning CT (AI\u2010pCT), enabling a clinically feasible simulation\u2010free workflow for spinal palliative radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Two neural networks were trained to correct spine position and body contour using paired dCT\u2013sCT images from 50 patients (42 train\/validation, 8 internal tests) in a safety net hospital and externally evaluated on 7 additional academic medical center (AMC) patients. After rigid bone\u2010based alignment to sCT, dosimetric accuracy was assessed by comparing DVH endpoints (Dmean, Dmax, D95, D99, V100, V107) and DVH Root\u2010Mean\u2010Square (RMS) error for plans recalculated on dCT versus AI\u2010pCT versus sCT. Four radiation oncologists scored image suitability. Significance was evaluated using the Wilcoxon signed\u2010rank test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the safety net cohort, AI\u2010pCT substantially reduced geometric and dosimetric error relative to dCT (e.g., Dmean error 2.0%\u21920.57%; RMS DVH error 6.4%\u21922.2%, all\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.05), improved physician plan\u2010quality ratings from \u201cAcceptable\u201d to \u201cGood\u2013Perfect,\u201d and increased plan\u2010level clinical goal achievement from 37.5% to 100%. In the AMC cohort, where baseline dCT was already closely aligned to sCT, AI\u2010pCT produced smaller but still statistically significant gains.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010pCT achieves sCT\u2010level geometric and dosimetric fidelity without requiring a separate simulation scan, enabling a simulation\u2010free planning workflow for spinal palliative RT. This approach has the potential to reduce treatment delays and improve access, particularly in resource\u2010constrained environments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70366","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:10:28Z","timestamp":1772986228000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simulation\u2010free spine palliative radiotherapy enabled by AI\u2010adapted diagnostic CT"],"prefix":"10.1002","volume":"53","author":[{"given":"Yiding","family":"Han","sequence":"first","affiliation":[{"name":"Radiation Oncology Department Baylor College of Medicine  TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander Nicola","family":"Hanania","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Baylor College of Medicine  TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaid Ali","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Baylor College of Medicine  TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Ugarte","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Baylor College of Medicine  TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boran","family":"Zhou","sequence":"additional","affiliation":[{"name":"Radiation Oncology Department Baylor College of Medicine  TX USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdallah S. 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