{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T03:51:37Z","timestamp":1791604297690,"version":"4.3.4"},"reference-count":34,"publisher":"Wiley","issue":"27","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"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":["82172866"],"award-info":[{"award-number":["82172866"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82373424"],"award-info":[{"award-number":["82373424"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019LZL019"],"award-info":[{"award-number":["ZR2019LZL019"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021LZL005"],"award-info":[{"award-number":["ZR2021LZL005"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012905","name":"Department of Science and Technology of Shandong Province","doi-asserted-by":"publisher","award":["2021CXGC011102"],"award-info":[{"award-number":["2021CXGC011102"]}],"id":[{"id":"10.13039\/100012905","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Thoracic Cancer"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the value of computed tomography (CT)\u2010based radiomics combined with clinical\u2010genetic features in predicting brain metastasis in patients with stage III\/IV epidermal growth factor receptor (EGFR)\u2010mutant non\u2010small\u2010cell lung cancer (NSCLC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The study included 147 eligible patients treated at our institution between January 2018 and May 2021. Patients were randomly divided into two cohorts for model training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009102) and validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200945). Radiomics features were extracted from the chest CT images before treatment, and a radiomics signature was constructed using the Least Absolute Shrinkage and Selection Operator regression. Kaplan\u2013Meier survival analysis was used to describe the differences in brain metastasis\u2010free survival (BM\u2010FS) risk. A clinical\u2010genetic model was developed using Cox regression analysis. Radiomics, genetic, and combined prediction models were constructed, and their predictive performances were evaluated by the concordance index (C\u2010index).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Patients with a low radiomics score had significantly longer BM\u2010FS than those with a high radiomics score in both the training (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) and the validation (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0016) cohorts. The C\u2010indices of the nomogram, which combined the radiomics signature and N stage, overall stage, third\u2010generation tyrosine kinase inhibitor treatment, and EGFR mutation status, were 0.886 (95% confidence interval [CI] 0.823\u20130.949) and 0.811 (95% CI 0.719\u20130.903) in the training and validation cohorts, respectively. The combined model achieved a higher discrimination and clinical utility than the single prediction models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined radiomics\u2010genetic model could be used to predict BM\u2010FS in stage III\/IV NSCLC patients with EGFR mutations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1759-7714.15410","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T04:59:42Z","timestamp":1722833982000},"page":"1919-1928","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Computed tomography\u2010based radiomics and clinical\u2010genetic features for brain metastasis prediction in patients with stage\n                    <scp>III<\/scp>\n                    \/\n                    <scp>IV<\/scp>\n                    epidermal growth factor receptor\u2010mutant non\u2010small\u2010cell lung cancer"],"prefix":"10.1111","volume":"15","author":[{"given":"Mei","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences  Jinan 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