{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:30:40Z","timestamp":1789471840728,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971113"],"award-info":[{"award-number":["31971113"]}],"id":[{"id":"10.13039\/501100001809","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":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone\u2010beam computed tomography (CBCT) was widely used in image\u2010guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index (SMI) of third lumbar\u00a0vertebra (L3). Whereas, L3 is usually not included on the cervical cancer radiotherapy CBCT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We aimed to explore the usefulness of CBCT for evaluating SMI and deep learning (DL)\u2010based automatic segmentation and sarcopenia diagnosis for cervical cancer radiotherapy patients. We evaluated the SMI through fifth\u00a0lumbar\u00a0vertebra (L5).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      First, L3, L5 skeletal muscle area (SMA) were measured on CT and CBCT. The agreement of L5 skeletal muscle segmentation on CBCT was evaluated using the intraclass correlation coefficient (ICC). The relationships between L5\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      and L3\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      , L5\u2010SMI\n                      <jats:sub>CBCT<\/jats:sub>\n                      were established and assessed by Pearson analysis, Bland\u2010Altman plots. Second, the consequent CBCT images of 248 cervical cancer radiotherapy patients with whole L5 were collected as DL\u2010based automatic segmentation. An independent external validation dataset was used. We proposed an end\u2010to\u2010end anatomical distance\u2010guided dual branch feature fusion network to segment L5 skeletal muscle on CBCT images. The automatic segmentation results were used for sarcopenia diagnosis evaluation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The ICC values were greater than 0.95. The Pearson correlation coefficients (PCC) between L5\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      and L3\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      is 0.894. The PCC between L5\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      and L5\u2010SMI\n                      <jats:sub>CBCT<\/jats:sub>\n                      is 0.917. The L3\u2010SMI\n                      <jats:sub>CT<\/jats:sub>\n                      could be estimated through L5\u2010SMI\n                      <jats:sub>CBCT<\/jats:sub>\n                      by a linear regression equation. The adjusted\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      values were greater than 0.7. The dice similarity coefficient of automatic segmentation is 87.09%. Our proposed DL network predicted sarcopenia with 84.38% accuracy and 85.71% F1\u2010score. In external validation dataset, the sarcopenia diagnosis accuracy and F1\u2010score are 80% and 82.61%, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The SMI quantitative measurement using CBCT for cervical cancer patients is feasible. And the DL network has the potential to assist in the sarcopenia diagnosis using CBCT images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17791","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:15:32Z","timestamp":1743725732000},"page":"2887-2897","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel skeletal muscle quantitative method and deep learning\u2010based sarcopenia diagnosis for cervical cancer patients treated with radiotherapy"],"prefix":"10.1002","volume":"52","author":[{"given":"Zhe","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Digital Medicine School of Biomedical Engineering and Medical Imaging Army Medical University (Third Military Medical University)  Chongqing China"},{"name":"Yu\u2010Yue Pathology Research Center Jinfeng Laboratory  Chongqing China"},{"name":"Department of Radiation Oncology Zigong First 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