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

Abstract 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.

Authors

Institutions

Publication Details

Journal
BMC Cancer
Published
2026-07-27
DOI
https://doi.org/10.1186/s12885-026-16468-3
Primary Topic
Cancer survivorship and care
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Xinyu Feng, Xiaopeng Ma, Lin Jiang, M N Liu et al.
BMC Cancer
Cancer survivorship and care
article

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

Xinyu Feng, Xiaopeng Ma, Lin Jiang, M N Liu, Jin Zhao, Liang Yu, Tian LD, Y L Guo, Qi Qin, Jing Jiang, Wenli Xie
article en

Abstract

Abstract 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.

BMC Cancer
University of Science and Technology of China (CN), Central South University (CN), Hong Kong Polytechnic University (HK), Bengbu Medical College (CN)
Reduced inequalities
Openalex Percentile: Top 12%
Cancer survivorship and care
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.