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1000 Titel
  • Identifying risk factors for cancer-specific early death in patients with advanced endometrial cancer: A preliminary predictive model based on SEER data
1000 Autor/in
  1. yang, Jing |
  2. Tian, Qi |
  3. Li, Guang |
  4. Liu, Qiao |
  5. Tang, Yi |
  6. Jiang, Dan |
  7. Shu, Chuqiang |
1000 Erscheinungsjahr 2025
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2025-02-12
1000 Erschienen in
1000 Quellenangabe
  • 20(2):e0318632
1000 Copyrightjahr
  • 2025
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0318632 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11819511/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • OBJECTIVE: To identify risk factors associated with cancer-specific early death in patients with advanced endometrial cancer and to develop a preliminary nomogram prediction model based on these factors, with an emphasis on the potential implications for clinical practice. METHODS: Patients from the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 2018 to 2021 were included in the study. The study data was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for cancer-specific early mortality in advanced endometrial cancer patients, and a preliminary nomogram prediction model was further constructed. The results of the Receiver Operating Characteristic (ROC) curve, calibration analysis, and clinical decision curve analysis (DCA) were presented for transparency. RESULTS: Significant risk factors for cancer-specific early death were identified, including tumor size (≥101 mm, OR = 2.11, P < 0.001), non-endometrioid histology (OR = 3.11, P < 0.001), high tumor grade (G3, OR = 2.68, P = 0.007), advanced tumor stages (T3-T4, OR = 1.84, P = 0.004), and metastatic stage (M1, OR = 2.05, P < 0.001), as well as the presence of liver metastases (OR = 2.21, P = 0.005) and brain metastases (OR = 8.08, P < 0.001). Protective factors that were significantly associated with a reduced risk of early death included hysterectomy (OR = 0.13, P = 0.012), radical surgery (OR = 0.21, P < 0.001), radiation therapy (OR = 0.40, P < 0.001), and chemotherapy (OR = 0.31, P < 0.001). A preliminary nomogram model was demonstrated adequate predictive performance with AUC values of 0.89 (95% CI 0.87 to 0.91) in the training cohort and 0.88 (95% CI 0.84 to 0.91) in the validation cohort. The model’s predictive performance was further supported by the calibration and DCA analyses, suggesting its potential clinical utility. CONCLUSION: This study identified key risk factors for early cancer-specific mortality in patients with advanced endometrial cancer. The preliminary nomogram model holds promise for predicting early death risk and could be valuable in clinical practice. Future work may explore its performance with additional data to ensure broad applicability.
1000 Sacherschließung
lokal Uterina cancer
lokal Surgical oncology
lokal Brain metastasis
lokal Cancer treatment
lokal Hepatocellular carcinoma
lokal Cancer immunotherapy
lokal Lung and intrathoracic tumors
lokal Cancer risk factors
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0009-0007-1501-2679|https://frl.publisso.de/adhoc/uri/VGlhbiwgUWk=|https://frl.publisso.de/adhoc/uri/TGksIEd1YW5n|https://frl.publisso.de/adhoc/uri/TGl1LCBRaWFv|https://frl.publisso.de/adhoc/uri/VGFuZywgWWk=|https://frl.publisso.de/adhoc/uri/SmlhbmcsIERhbg==|https://frl.publisso.de/adhoc/uri/U2h1LCBDaHVxaWFuZw==
1000 (Academic) Editor
1000 Label
1000 Förderer
  1. Science and Technology Program of Hunan Province |
  2. Natural Science Foundation of Hunan Province Department Joint Fund Project |
  3. Hunan Provincial Natural Science Foundation Medical and Health Industry Joint Fund |
1000 Fördernummer
  1. 2021SK4021
  2. 2023JJ60013
  3. 2024JJ9334
1000 Förderprogramm
  1. -
  2. -
  3. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Science and Technology Program of Hunan Province |
    1000 Förderprogramm -
    1000 Fördernummer 2021SK4021
  2. 1000 joinedFunding-child
    1000 Förderer Natural Science Foundation of Hunan Province Department Joint Fund Project |
    1000 Förderprogramm -
    1000 Fördernummer 2023JJ60013
  3. 1000 joinedFunding-child
    1000 Förderer Hunan Provincial Natural Science Foundation Medical and Health Industry Joint Fund |
    1000 Förderprogramm -
    1000 Fördernummer 2024JJ9334
1000 Objektart article
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1000 Erstellt am 2025-03-14T12:22:01.599+0100
1000 Erstellt von 337
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