{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:47:29Z","timestamp":1786985249397,"version":"build-2736575974"},"reference-count":40,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 176 patients were enrolled in this study (training group, n\u2009=\u2009123; validation group, n\u2009=\u200953). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial\u2013clinical model (Model 5), a venous-clinical model (Model 6) and an arterial\u2013venous\u2013clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Tumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve]\u2009=\u20090.721, 95% CI [confidence intervals], 0.630\u20130.813; validation group, AUC\u2009=\u20090.729, 95% CI, 0.593\u20130.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC\u2009=\u20090.850 and 0.869, 95% CI, 0.782\u20130.919 and 0.807\u20130.932, respectively;\n                      <jats:italic toggle=\"yes\">p\u2009<\/jats:italic>\n                      =\u20090.250) and validation groups (AUC\u2009=\u20090.780 and 0.716, 95% CI, 0.653\u20130.906 and 0.576\u20130.856, respectively;\n                      <jats:italic toggle=\"yes\">p\u2009<\/jats:italic>\n                      =\u20090.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08953996241313322","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T02:05:22Z","timestamp":1740535522000},"page":"553-564","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer"],"prefix":"10.1177","volume":"33","author":[{"given":"Xia","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-li","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sichuan Academy of 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