{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:40:17Z","timestamp":1790354417040,"version":"4.1.0"},"reference-count":38,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82060429"],"award-info":[{"award-number":["82060429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJOG"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity\u2010modulated radiation therapy and brachytherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>A retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Xiangya Hospital of Central South University, Changsha, Hunan, China.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Two hundred and fifty\u2010seven LACC patients who were treated with intensity\u2010modulated radiotherapy from 2014 to 2017.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients were allocated into the training\/validation sets (3:1 ratio) using proportional random sampling, resulting in the same proportion of groups in the two sets. We extracted 254 radiomic features from each of the gross target volume, pelvis and sacral vertebrae. The sequentially backward elimination support vector machine algorithm was used for feature selection and end point prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main outcomes and measures<\/jats:title>\n                    <jats:p>Clinical end points include tumour complete response (CR), 5\u2010year overall survival (OS), anaemia, and leucopenia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A combination of ten clinicopathological parameters and 34 radiomic features performed best for predicting CR (validation balanced accuracy: 80.8%). The validation balanced accuracy of 54 radiomic features was 85.8% for OS, and their scores can stratify patients into the low\u2010risk and high\u2010risk groups (5\u2010year OS: 95.5% versus 36.4%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The clinical and radiomic models were also predictive of anaemia and leucopenia (validation balanced accuracies: 71.0% and 69.9%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrated that combining clinicopathological parameters with CT\u2010based radiomics may have value for predicting clinical end points in LACC. If validated, this model may guide therapeutic strategy to optimise the effectiveness and minimise toxicity or treatment for LACC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1471-0528.17285","type":"journal-article","created":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T02:34:22Z","timestamp":1662172462000},"page":"222-230","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study"],"prefix":"10.1111","volume":"130","author":[{"given":"Kang","family":"Ren","sequence":"first","affiliation":[{"name":"Department of Oncology, National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital Central South University  Changsha China"},{"name":"Department of Radiation Oncology, Peking Union Medical College Hospital Chinese Academy of Medical Sciences &amp; 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