{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T21:52:15Z","timestamp":1790545935110,"version":"4.1.0"},"reference-count":57,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T00:00:00Z","timestamp":1597104000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Anatomical variations occur during head and neck (H&amp;N) radiotherapy treatment. kV cone\u2010beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo\u2010CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&amp;N cancer radiotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Forty\u2010four patients received VMAT for H&amp;N cancer (70\u201063\u201056\u00a0Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU\u2010D curve method), (b) a water\u2013air\u2010bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT\n                      <jats:sub>ref<\/jats:sub>\n                      ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%\/2\u00a0mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT\n                      <jats:sub>ref<\/jats:sub>\n                      and pCT of each method.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the entire body, the MAEs and MEs of the DLM, HU\u2010D curve method, DAM, and DIR method were 82.4 and 17.1\u00a0HU, 266.6 and 208.9\u00a0HU, 113.2 and 14.2\u00a0HU, and 95.5 and \u221236.6\u00a0HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0\u2264\u00a00.05). The DLM dose discrepancies were 7\u00a0\u00b1\u00a08\u00a0cGy (maximum\u00a0=\u00a044\u00a0cGy) for the ipsilateral parotid gland D\n                      <jats:sub>mean<\/jats:sub>\n                      and 5\u00a0\u00b1\u00a06\u00a0cGy (max\u00a0=\u00a026\u00a0cGy) for the contralateral parotid gland mean dose (D\n                      <jats:sub>mean<\/jats:sub>\n                      ). For the parotid gland D\n                      <jats:sub>mean<\/jats:sub>\n                      , no significant dose difference was observed between the DLM and other methods.\n                    <\/jats:p>\n                    <jats:p>The mean 3D gamma pass rate\u00a0\u00b1\u00a0standard deviation was 98.1\u00a0\u00b1\u00a01.2%, 91.0\u00a0\u00b1\u00a05.3%, 97.9\u00a0\u00b1\u00a01.6%, and 98.8\u00a0\u00b1\u00a00.7% for the DLM, HU\u2010D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU\u2010D curve method, DAM, and DIR method differed significantly from those of the DLM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>For H&amp;N radiotherapy, DIR method and DLM appears as the most appealing CBCT\u2010based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.14387","type":"journal-article","created":{"date-parts":[[2020,7,12]],"date-time":"2020-07-12T03:09:30Z","timestamp":1594523370000},"page":"4683-4693","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Comparison of CBCT\u2010based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning"],"prefix":"10.1002","volume":"47","author":[{"given":"Ana\u00efs","family":"Barateau","sequence":"first","affiliation":[{"name":"Univ. 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