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Background <p>Fear of progression (FoP) is a prevalent psychological response to the real threats associated with cancer diagnosis, treatment, and disease trajectory, and it represents one of the most prominent sources of distress symptoms among patients with breast cancer. Persistently elevated FoP may not only negatively impact treatment outcomes but also contribute to excessive healthcare utilization and reduced quality of life. This study aimed to construct and validate a predictive model for FoP in postoperative breast cancer patients based on the Health Ecology Model.</p> Methods <p>This multi-center cross-sectional study enrolled 347 postoperative breast cancer patients from three tertiary Grade-A general hospitals in Anhui Province, China, between March 25, 2024, and June 25, 2024. Univariable analysis and multivariable logistic regression were employed to identify independent predictors of FoP. A nomogram was then constructed based on these predictors and internally validated. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), respectively.</p> Results <p>The prevalence of high FoP was 52.74%. Multivariable logistic regression identified chemotherapy, frequency of physical activity, work status, sense of coherence, illness perception, and coping mode as independent predictors of FoP, which were incorporated into the nomogram. The model demonstrated excellent discriminatory power, with areas under the ROC curve (AUC) of 0.867 and 0.898 for the training and validation sets, respectively. Calibration plots indicated good agreement between predicted and observed outcomes, while DCA indicated favorable clinical utility across a range of threshold probabilities.</p> Conclusions <p>A predictive model for FoP among postoperative breast cancer patients was successfully developed and validated within the HEM framework. This model may facilitate the early identification of individuals at high risk for FoP and provide a foundation for targeted interventions aimed at alleviating psychological distress and improving overall well-being.</p>

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Construction and validation of a predictive model for fear of progression among postoperative patients with breast cancer: a multi-center cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Lingyun Tian,
  • Xinyu Feng,
  • Mengyuan Liu,
  • Jing Jiang,
  • Qi Qin,
  • Wenli Xie,
  • Yang Guo,
  • Jing Zhao,
  • Yu Liang,
  • Xiaopeng Ma,
  • Lin Jiang

摘要

Background

Fear of progression (FoP) is a prevalent psychological response to the real threats associated with cancer diagnosis, treatment, and disease trajectory, and it represents one of the most prominent sources of distress symptoms among patients with breast cancer. Persistently elevated FoP may not only negatively impact treatment outcomes but also contribute to excessive healthcare utilization and reduced quality of life. This study aimed to construct and validate a predictive model for FoP in postoperative breast cancer patients based on the Health Ecology Model.

Methods

This multi-center cross-sectional study enrolled 347 postoperative breast cancer patients from three tertiary Grade-A general hospitals in Anhui Province, China, between March 25, 2024, and June 25, 2024. Univariable analysis and multivariable logistic regression were employed to identify independent predictors of FoP. A nomogram was then constructed based on these predictors and internally validated. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), respectively.

Results

The prevalence of high FoP was 52.74%. Multivariable logistic regression identified chemotherapy, frequency of physical activity, work status, sense of coherence, illness perception, and coping mode as independent predictors of FoP, which were incorporated into the nomogram. The model demonstrated excellent discriminatory power, with areas under the ROC curve (AUC) of 0.867 and 0.898 for the training and validation sets, respectively. Calibration plots indicated good agreement between predicted and observed outcomes, while DCA indicated favorable clinical utility across a range of threshold probabilities.

Conclusions

A predictive model for FoP among postoperative breast cancer patients was successfully developed and validated within the HEM framework. This model may facilitate the early identification of individuals at high risk for FoP and provide a foundation for targeted interventions aimed at alleviating psychological distress and improving overall well-being.