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Purpose <p>To evaluate the performance and methodological quality of published diagnostic prediction models for cancer-related fatigue (CRF), and to provide evidence for clinical practice and future research.</p> Methods <p>A systematic review and meta-analysis were conducted. PubMed, Web of Science, the Cochrane Library, Embase, and Scopus were searched from inception to October 18, 2024, for studies developing or validating diagnostic prediction models for CRF. The pooled area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI) were calculated using R. Heterogeneity was assessed using the <i>I</i><sup>2</sup> statistic and Cochran’s <i>Q</i> test, publication bias was explored using funnel plots and Egger’s test, and risk of bias was evaluated with the Prediction Model Risk of Bias Assessment Tool (PROBAST).</p> Results <p>A total of 8418 records were identified, of which 13 studies met the inclusion criteria. These studies included 23 cohorts, 444,447 cancer patients, and 11 diagnostic prediction models. The pooled AUC was 0.83 (95% CI = 0.78–0.87), indicating moderate-to-good discrimination. However, substantial heterogeneity was observed (<i>I</i><sup>2</sup> &gt; 94%), suggesting that the pooled estimate should be interpreted with caution. PROBAST assessment indicated a high risk of bias in most studies, mainly related to statistical analysis and reporting. Egger’s test suggested possible funnel plot asymmetry, but this finding should be considered exploratory because of the small number of studies and high heterogeneity.</p> Conclusion <p>Existing CRF diagnostic prediction models show moderate-to-good discrimination in research settings, but their performance varies across populations, outcome definitions, and modeling approaches. Future studies should prioritize large-scale, multi-center, multiethnic, and externally validated models to improve early identification and precise management of CRF.</p>

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Diagnostic predictive models for cancer-related fatigue: current evidence and future directions

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Fanqi Liang,
  • Menglu Wang,
  • Keke Li,
  • Deliang Lv,
  • Taoming Qian,
  • Zhijun Bu

摘要

Purpose

To evaluate the performance and methodological quality of published diagnostic prediction models for cancer-related fatigue (CRF), and to provide evidence for clinical practice and future research.

Methods

A systematic review and meta-analysis were conducted. PubMed, Web of Science, the Cochrane Library, Embase, and Scopus were searched from inception to October 18, 2024, for studies developing or validating diagnostic prediction models for CRF. The pooled area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI) were calculated using R. Heterogeneity was assessed using the I2 statistic and Cochran’s Q test, publication bias was explored using funnel plots and Egger’s test, and risk of bias was evaluated with the Prediction Model Risk of Bias Assessment Tool (PROBAST).

Results

A total of 8418 records were identified, of which 13 studies met the inclusion criteria. These studies included 23 cohorts, 444,447 cancer patients, and 11 diagnostic prediction models. The pooled AUC was 0.83 (95% CI = 0.78–0.87), indicating moderate-to-good discrimination. However, substantial heterogeneity was observed (I2 > 94%), suggesting that the pooled estimate should be interpreted with caution. PROBAST assessment indicated a high risk of bias in most studies, mainly related to statistical analysis and reporting. Egger’s test suggested possible funnel plot asymmetry, but this finding should be considered exploratory because of the small number of studies and high heterogeneity.

Conclusion

Existing CRF diagnostic prediction models show moderate-to-good discrimination in research settings, but their performance varies across populations, outcome definitions, and modeling approaches. Future studies should prioritize large-scale, multi-center, multiethnic, and externally validated models to improve early identification and precise management of CRF.