Objective  To analyze the influencing factors of fertility anxiety (FA) in female patients of reproductive age with thyroid cancer and construct a corresponding risk prediction model.

Methods  This was a prospective study. Female patients of childbearing age with thyroid cancer who attended the Surgery Department of Thyroid Hernia, Guangdong Provincial People's Hospital Affiliated to Southern Medical University from April 2024 to April 2025 were enrolled as research subjects. A general information questionnaire and the Reproductive Concerns After Cancer (RCAC) Scale were used to conduct a questionnaire survey. The patients were randomly divided into a training set and a validation set at a ratio of 7 ∶ 3, and stratified into the high fertility anxiety (HFA) group and low fertility anxiety (LFA) group according to the median RCAC score of the training set. Univariate and multivariate binary Logistic regression analyses were performed to identify the influencing factors of HFA in childbearing age patients with thyroid cancer, and a risk prediction model for HFA was established. The receiver operating characteristic (ROC) curve with its area under the curve (AUC), calibration curve and decision curve were used to evaluate the predictive efficacy, accuracy and clinical applicability of the model.

Results  A total of 390 female childbearing age patients with thyroid cancer were enrolled, with 273 cases in the training set and 117 cases in the validation set. The total RCAC score was (53.3±7.6) points. Multivariate analysis showed that age ≥30 years [OR=0.22, 95%CI (0.12, 0.41)], average monthly household income >10,000 yuan [OR=0.35, 95%CI (0.15, 0.87)] and having at least one child [OR=0.16, 95%CI (0.08, 0.32)] were significantly associated with a reduced risk of HFA; while divorced marital status [OR=3.77, 95%CI (1.41, 10.08)], unmarried marital status [OR=3.41, 95%CI (1.70, 6.85)] and disease duration >6 months [OR=4.81, 95%CI (2.23, 10.39)] were significantly associated with an increased risk of HFA. ROC curve analysis indicated that the AUC values of the HFA risk prediction model for patients of reproductive age with thyroid cancer were 0.83 and 0.82 in the training set and internal validation set, respectively. The calibration curve and the clinical decision curve showed that the prediction model had high accuracy and clinical net benefit.

Conclusion  The HFA risk prediction model constructed in this study has good discrimination and calibration ability, which may provide a convenient tool for clinicians to early identify reproductive age patients with thyroid cancer who are at high risk of HFA. However, multicenter external validation is needed to further evaluate the stability and generalizability of this model.

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