{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T02:05:21Z","timestamp":1790820321720,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"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":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Deep learning\u2010based (DL) approaches have gained interest in predicting dose distributions in radiotherapy of prostate cancer treated with volumetric modulated arc therapy and intensity\u2010modulated radiation therapy. Meanwhile, research on predicting dose distributions in high\u2010precision stereotactic radiotherapy treatments has remained relatively underrepresented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We aimed to expand the previous studies by developing a DL\u2010based framework for predicting dose distributions for robotic, stereotactic prostate cancer radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We harnessed a U\u2010Net\u2010based convolutional neural network for predicting clinically achievable dose distributions based on CT images, delineated structures, and distance information from the planning target volume. A dataset of 462 patients treated with CyberKnife (Accuray Inc.) utilizing an Iris collimator was divided into training (70%,\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0323), validation (10%,\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a046), and test (20%,\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a093) sets.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the independent test set, the mean absolute error between the mean doses of predictions and clinical plans was 0.63\u00a0Gy for the rectum and 1.04\u00a0Gy for the bladder.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed U\u2010Net\u2010based model demonstrated the ability to learn and reproduce characteristic dose distributions in CyberKnife prostate cancer radiotherapy. The model may provide patient\u2010specific dose estimates for setting initial planning objectives to assist in automating treatment planning and improving inter\u2010planner consistency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70784","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T01:26:11Z","timestamp":1790817971000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning\u2010based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife"],"prefix":"10.1002","volume":"27","author":[{"given":"Hilla","family":"Magga","sequence":"first","affiliation":[{"name":"Department of Technical Physics University of Eastern Finland  Kuopio Finland"},{"name":"Center of Oncology Kuopio University Hospital  Kuopio 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