{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:00:00Z","timestamp":1775581200794,"version":"3.50.1"},"reference-count":25,"publisher":"Georg Thieme Verlag KG","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nuklearmedizin"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:p>\n                  Purpose\u2002The aim of this study was to investigate the potential of multiparametric 18F-FDG PET\/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients.<\/jats:p><jats:p>\n                  Materials and Methods\u2002A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET\/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language.<\/jats:p><jats:p>\n                  Results\u2002Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91\u200a% and specificity of 92\u200a%. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83\u200a%, specificity of 67\u200a% and an AUC of 0.82.<\/jats:p><jats:p>\n                  Conclusion\u2002M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET\/MRI data.<\/jats:p><jats:p>\n                  Key points:\u2002\n               <\/jats:p>","DOI":"10.1055\/a-2157-6867","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T23:30:25Z","timestamp":1707348625000},"page":"34-42","source":"Crossref","is-referenced-by-count":5,"title":["Radiomics Analysis of Multiparametric PET\/MRI for N- and M-Staging in Patients with Primary Cervical Cancer"],"prefix":"10.1055","volume":"63","author":[{"given":"Lale","family":"Umutlu","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Nensa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aydin","family":"Demircioglu","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerald","family":"Antoch","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, D-40225\u00a0Dusseldorf, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Herrmann","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, D-45147 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Forsting","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147 Essen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes Stefan","family":"Grueneisen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic 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