Construction and validation of the prediction model for fear of cancer recurrence in patients with postoperative cervical cancer

TL;DR

This study creates a model to predict fear of cancer recurrence in cervical cancer patients after surgery, showing it is effective and could help with personalized risk assessments.

Contribution

A novel nomogram model was developed and validated for predicting fear of cancer recurrence in postoperative cervical cancer patients.

Findings

174 out of 310 patients (56.12%) exhibited fear of cancer recurrence.

The model achieved an AUC of 0.910 with high sensitivity and specificity.

Variables like age, education, treatment, and social support significantly influenced fear of recurrence.

Abstract

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.

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.

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(P < 0.05). Subsequently, a predictive model was established based on these factors. The model’s goodness-of-fit was assessed using the Hosmer-Lemeshow test, yielding a χ² value of 6.773(P = 0.610). The area under the receiver operating characteristic curve(AUC) was determined to be 0.910(95%CI 0.853-0.966), with a sensitivity of 87.5% and specificity of 81.2%.

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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