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A second reader extracted features in 154\/489 patients, in which intraclass correlation coefficients (ICCs) were calculated. Radiomic features with ICCs\u2009&gt;\u20090.75 were retained and ComBat harmonization was applied to reduce scanner\/protocol effects on the extracted features. Patients were divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009343) and test (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009146) sets. A radiomic DSS score (R\n                      <jats:sub>dss<\/jats:sub>\n                      ) was developed in the training set using least absolute shrinkage and selection operator (LASSO) Cox regression. A combined model (C\n                      <jats:sub>dss<\/jats:sub>\n                      ), incorporating R\n                      <jats:sub>dss<\/jats:sub>\n                      , PET positive lymph nodes (LN\n                      <jats:sub>PET<\/jats:sub>\n                      ) and preoperative histology risk was constructed using multivariable Cox hazard analyses. Prediction performances were assessed by comparing areas under time-dependent receiver operating characteristic curves (tdROCs AUCs) for R\n                      <jats:sub>dss<\/jats:sub>\n                      , C\n                      <jats:sub>dss<\/jats:sub>\n                      , and conventional PET markers: SUV\n                      <jats:sub>max<\/jats:sub>\n                      , SUV\n                      <jats:sub>mean<\/jats:sub>\n                      , MTV, tumor lesion glycolysis (TLG) and LN\n                      <jats:sub>PET<\/jats:sub>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the test set, AUCs for 2- and 5-year DSS were higher for R\n                      <jats:sub>dss<\/jats:sub>\n                      (0.855, 0.720) compared to SUV\n                      <jats:sub>max<\/jats:sub>\n                      (0.548, 0.572) and SUV\n                      <jats:sub>mean<\/jats:sub>\n                      (0.549, 0.554) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u20090.04 for all), while similar to MTV (0.863, 0.696), TLG (0.814, 0.672) and LN\n                      <jats:sub>PET<\/jats:sub>\n                      (0.802, 0.626) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2265\u20090.12 for all). C\n                      <jats:sub>dss<\/jats:sub>\n                      predicted 2-year DSS with AUC of 0.909 in the test set, outperforming all conventional imaging markers (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u20090.04 for all) except MTV (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.29). For 5-year DSS, C\n                      <jats:sub>dss<\/jats:sub>\n                      (AUC: 0.817) outperformed all conventional imaging markers, including MTV (AUC\u2009\u2264\u20090.696,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009\u2264\u20090.05, for all).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      R\n                      <jats:sub>dss<\/jats:sub>\n                      predicts short-term survival with high accuracy, outperforming tumor SUV\n                      <jats:sub>max\/mean<\/jats:sub>\n                      , but not MTV, TLG and LN\n                      <jats:sub>PET<\/jats:sub>\n                      . The combined C\n                      <jats:sub>dss<\/jats:sub>\n                      model yields high accuracy for predicting both short- and long-term survival, outperforming all conventional PET imaging markers.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07335-7","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T04:29:06Z","timestamp":1749616146000},"page":"167-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting aggressive disease and poor outcome in endometrial cancer using preoperative [18F]FDG PET primary tumor radiomics"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-7870","authenticated-orcid":false,"given":"Kristine Eldevik","family":"Fasmer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankush","family":"Gulati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunniva","family":"Lind\u00e5s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camilla","family":"Krakstad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ingfrid Salvesen","family":"Haldorsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"7335_CR1","unstructured":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries - Bray - 2024 - CA: A Cancer Journal for Clinicians - Wiley Online Library [Internet]. 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