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Peer-reviewedCC BY 4.0Frontiers in Psychiatry

Risk prediction models for fear of cancer recurrence in patients with cancer: a systematic review

  • Yifan Zhang1,2
  • Dongmei Wu1,2
  • Wendan Xiao1,2
  • Mao Luo1,2
  • Lingyan Zhou1,2
  • Yuanyuan Fan1,2
  • Baolian Xu1,2
  • Yanjun Li1,2
  • Lihong Zhao1,2
  1. 1Department of Radiology, West China Hospital, Sichuan University
  2. 2West China School of Nursing, Sichuan University
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Abstract

Background: The apprehension of cancer recurrence among cancer survivors strongly influences their quality of life. Predictive models can enable the timely recognition of this concern and the implementation of interventions. In recent years, a multitude of models have been constructed to predict the likelihood of disease recurrence in cancer patients. Nevertheless, the methodological strictness and clinical applicability of these models necessitate further elucidation.

Objectives: Systematic assessment of the existing predictive models for fear of cancer recurrence.

Methods: FOUR databases, including PubMed, the Cochrane Library, Embase, and Web of Science, were systematically searched to identify relevant studies published from the inception of the databases to December 31, 2025. Data extraction was independently performed by two reviewers on the basis of the critical evaluation and data extraction checklist of the systematic review predictive model study. The quality of the included models was assessed using the predictive model bias risk assessment tool.

Results: A total of 1,137 articles were retrieved, from which 15 models across 9 studies were ultimately selected for inclusion. Logistic Regression and machine learning were the most frequently employed methods for model development. The AUC values of the developed models ranged from 0.66 to 0.9786, whereas those reported in validation studies ranged from 0.83 to 0.949. None of the models underwent external validation. All risk prediction models exhibited high bias, primarily because of inappropriate data sources and methodological shortcomings. Social support, age, and education level were identified as common predictive factors.

Conclusions: Current predictive models for the risk of cancer recurrence exhibit good discriminative power. However, most of the included studies are single-center retrospective studies with small sample sizes and lack external validation; these studies exhibit significant methodological limitations that restrict their clinical applicability. Future researchers are encouraged to develop predictive models in accordance with the PROBAST guidelines, enhance methodological rigor, and conduct both internal and external validation.

Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/recorddashboard , identifier CRD420261288735.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026risk,
  title = {{Risk prediction models for fear of cancer recurrence in patients with cancer: a systematic review}},
  author = {Yifan Zhang and Dongmei Wu and Wendan Xiao and Mao Luo and Lingyan Zhou and Yuanyuan Fan and Baolian Xu and Yanjun Li and Lihong Zhao},
  journal = {Frontiers in Psychiatry},
  year = {2026},
  month = oct,
  doi = {10.3389/fpsyt.2026.1940840},
  url = {https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1940840/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Risk prediction models for fear of cancer recurrence in patients with cancer: a systematic review
AU  - Yifan Zhang
AU  - Dongmei Wu
AU  - Wendan Xiao
AU  - Mao Luo
AU  - Lingyan Zhou
AU  - Yuanyuan Fan
AU  - Baolian Xu
AU  - Yanjun Li
AU  - Lihong Zhao
PY  - 2026
DA  - 2026/10/22
JO  - Frontiers in Psychiatry
DO  - 10.3389/fpsyt.2026.1940840
UR  - https://www.frontiersin.org/articles/10.3389/fpsyt.2026.1940840/full
AB  - Background: The apprehension of cancer recurrence among cancer survivors strongly influences their quality of life. Predictive models can enable the timely recognition of this concern and the implementation of interventions. In recent years, a multitude of models have been constructed to predict the likelihood of disease recurrence in cancer patients. Nevertheless, the methodological strictness and clinical applicability of these models necessitate further elucidation.  Objectives: Systematic assessment of the existing predictive models for fear of cancer recurrence.  Methods: FOUR databases, including PubMed, the Cochrane Library, Embase, and Web of Science, were systematically searched to identify relevant studies published from the inception of the databases to December 31, 2025. Data extraction was independently performed by two reviewers on the basis of the critical evaluation and data extraction checklist of the systematic review predictive model study. The quality of the included models was assessed using the predictive model bias risk assessment tool.  Results: A total of 1,137 articles were retrieved, from which 15 models across 9 studies were ultimately selected for inclusion. Logistic Regression and machine learning were the most frequently employed methods for model development. The AUC values of the developed models ranged from 0.66 to 0.9786, whereas those reported in validation studies ranged from 0.83 to 0.949. None of the models underwent external validation. All risk prediction models exhibited high bias, primarily because of inappropriate data sources and methodological shortcomings. Social support, age, and education level were identified as common predictive factors.  Conclusions: Current predictive models for the risk of cancer recurrence exhibit good discriminative power. However, most of the included studies are single-center retrospective studies with small sample sizes and lack external validation; these studies exhibit significant methodological limitations that restrict their clinical applicability. Future researchers are encouraged to develop predictive models in accordance with the PROBAST guidelines, enhance methodological rigor, and conduct both internal and external validation.  Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/recorddashboard , identifier CRD420261288735.
SN  - 1664-0640
ER  -