{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T05:38:27Z","timestamp":1792993107204,"version":"4.5.0"},"reference-count":25,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":["Medical Physics"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In pencil beam scanning (PBS) proton therapy, treatment plans are encoded as proton spot maps (PSMs). Although deep\u2010learning methods can rapidly predict three\u2010dimensional (3D) dose distributions from patient anatomy and planning intent, there is still no widely adopted direct, non\u2010iterative approach to convert doses into physically deliverable spot patterns. This limits end\u2010to\u2010end automation in treatment planning and adaptive replanning for PBS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We developed and retrospectively evaluated GenSpot, a two\u2010stage framework that reconstructs a PBS PSM from CT and dose. This study focused on the dose\u2010to\u2010spots reconstruction step under controlled, achievable\u2010dose conditions, using clinical Monte Carlo (MC) dose distributions as input in a single\u2010institution prostate stereotactic body radiation therapy (SBRT) cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>GenSpot uses a physics\u2010informed projected proton spot map (PrPSM) representation that projects individual proton spots through the CT using water\u2010equivalent thickness and percentage depth dose (PDD) information, aligning the spots with the CT and dose voxel grid while preserving a linear relationship with spot weights. Our dataset comprised 1036 fields from 259 prostate SBRT plans, split into training, validation, and test sets in an 80%\/10%\/10% ratio. A 3D SwinUNETR model was trained to predict PrPSMs from CT and dose inputs. Field\u2010specific PSMs were then reconstructed from the predicted PrPSMs using column\u2010wise nonnegative Lasso regression with precomputed PDD curves. MC dose distributions from GenSpot and clinical PSMs were compared using voxel\u2010wise mean absolute error (MAE), 3D gamma analysis (3%\/3\u00a0mm, 10% low\u2010dose threshold), and dose\u2010volume histogram (DVH) metrics at the composite\u2010plan level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Within the held\u2010out test dataset, the selected SwinUNETR model achieved a mean PrPSM MAE of 0.06\u00a0\u00b1\u00a00.02 (normalized units) with high structural similarity to projected clinical PrPSMs. At the dose level, MC doses from GenSpot PSMs showed low MAE (0.07\u00a0\u00b1\u00a00.03\u00a0Gy in the nonzero\u2010dose region) and high mean gamma passing rates (0.92\u00a0\u00b1\u00a00.05 at the field level and 0.99\u00a0\u00b1\u00a00.02 at the plan level). Composite\u2010plan DVH metrics differences were generally within 1\u00a0Gy for targets and organs at risk, although the high\u2010dose tail of the clinical target volume showed a modest systematic increase. Spot map complexity, measured by the number of energy layers and spot counts, was similar to that of clinical plans, with 20% more spots for GenSpot PSMs. For a four\u2010field case, GenSpot PSM generation required approximately 2.9\u00a0\u00b1\u00a00.2\u00a0min excluding final MC recalculation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>GenSpot reconstructed machine\u2010compatible PSMs from CT and clinical MC dose with close composite\u2010plan dose agreement in a single\u2010institution prostate SBRT cohort. These results support GenSpot as a rapid, physics\u2010informed dose\u2010to\u2010spots reconstruction component under achievable\u2010dose conditions. The present study does not establish performance for arbitrary predicted or adapted dose inputs, and final MC recalculation and standard QA remain required before clinical use. Further validation with non\u2010idealized dose inputs, multi\u2010institution data, higher\u2010resolution dose representations, and dedicated optimization\u2010based baselines is needed before broader clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70693","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T10:57:37Z","timestamp":1791543457000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A two\u2010stage framework for fast proton spot map generation in pencil beam scanning prostate SBRT planning"],"prefix":"10.1002","volume":"53","author":[{"given":"Xueyan","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hok Wan Chan","family":"Tseung","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Pepin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiasen","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David M.","family":"Routman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doug J.","family":"Moseley","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon","family":"Reber","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jed E.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Mayo Clinic  Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2006.10.0131"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/pro6.1149"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15092555"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaf5e9"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14132"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3679340"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac344f"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190594"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13526"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2017.02.007"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac9cb3"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.737901"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/adeb90"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16408"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16431"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad48f6"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17794"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4921046"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2016.03.001"},{"key":"e_1_2_8_21_1","doi-asserted-by":"crossref","unstructured":"CaoH WangY ChenJ et\u00a0al.Swin\u2010unet: Unet\u2010like pure transformer for medical image segmentation. 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