ArticleFrontiers in oncology2025
Construction and validation of the prediction model for fear of cancer recurrence in patients with postoperative cervical cancer.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Determinants of Fear of Cancer Recurrence and Development of a Nomogram-Based Risk Prediction Model in Patients with Primary Liver Cancer: A Single-Center Cross-Sectional Study.Journal of hepatocellular carcinoma · 2026Article
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8 authors.
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Abstract
Objectives: To construct a nomogram model for predicting the danger of fear of cancer recurrence in postoperative cervical cancer patients and to verify the predictive efficacy of the model. Methods: A total of 310 patients who underwent cervical cancer surgery at the Gynecologic Oncology Department of the First Affiliated Hospital of Bengbu Medical University from May 2024 to December 2024. The influencing factors were screened using single and multifactor stepwise logistic regression analysis. A nomogram prediction model was built using these predictors. Using 1,000 bootstrap resamples and the area under the curve(AUC) of the participants' operating characteristics, the model's effectiveness was confirmed. Results: Within the study population, 174 out of 310 patients(56.12%)exhibited a fear of cancer recurrence. Multifactorial analysis highlighted that variables such as age, educational level, treatment modality, Social Support Rate Scale(SSRS), and monthly family income significantly influenced fear of cancer recurrence in patients with postoperative cervical cancer( Conclusion: The research results indicate that the incidence of fear of cancer recurrence is high among them. Furthermore, the developed prediction model's high predictive efficacy, suggesting its potential utility for individualized risk assessment concerning fear of cancer recurrence in this patient population. This model was developed and validated in a single-center cohort, and its generalizability requires future external validation.
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