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Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To develop and validate an axial T\n                      <jats:sub>1<\/jats:sub>\n                      contrast\u2010enhanced (CE) MR\u2010based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>\n                      T\n                      <jats:sub>1<\/jats:sub>\n                      CE MRI sequences at 1.5T.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      The Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      \u2010test and the chi\u2010square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum\u2010redundancy\/maximum\u2010relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non\u2010LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326\u20130.8745) in the training cohort and 0.727 (95% CI, 0.5449\u20130.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>\n                      T\n                      <jats:sub>1<\/jats:sub>\n                      CE MR\u2010based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Level of Evidence<\/jats:bold>\n                      : 4\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Technical Efficacy<\/jats:bold>\n                      : Stage 2\n                    <\/jats:p>\n                    <jats:p>J. Magn. Reson. Imaging 2019;49:1420\u20131426.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.26531","type":"journal-article","created":{"date-parts":[[2018,10,26]],"date-time":"2018-10-26T07:53:36Z","timestamp":1540540416000},"page":"1420-1426","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["MR\u2010Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph\u2010Vascular Space Invasion preoperatively"],"prefix":"10.1002","volume":"49","author":[{"given":"Zhicong","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiology, Shanghai General Hospital Shanghai Jiao Tong University School of Medicine  Shanghai P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailin","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Molecular Imaging, Institute of Automation Chinese Academy of Sciences  Beijing P.R. 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