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Background <p>The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be associated with the progression and prognosis of various malignancies. However, its prognostic significance in patients with lung cancer remains controversial.</p> Methods <p>We conducted a systematic evaluation to investigate the prognostic value of the HALP score in relation to survival outcomes in patients with lung cancer. A comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library was performed up to October, 2025, to identify studies assessing the association between HALP and lung cancer prognosis. Pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were calculated for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-free survival (DFS).</p> Results <p>A total of 17 studies involving 10,635 lung cancer patients were included. Specifically, 15 studies (10,238 patients) assessed OS, 6 studies (1173 patients) assessed PFS, and 3 studies (362 patients) assessed DFS/RFS. The pooled analysis revealed that a low HALP score was significantly associated with poorer OS (HR 1.82, 95%CI 1.46–2.28, <i>p</i> &lt; 0.001), PFS (HR 1.66, 95%CI 1.17–2.35, <i>p</i> &lt; 0.001), and DFS/RFS (HR 2.78, 95%CI 1.14–6.78, <i>p</i> &lt; 0.001). Subgroup analyses further confirmed that pretreatment HALP was an independent predictor of OS in lung cancer patients.</p> Conclusions <p>A lower pretreatment HALP score was significantly associated with poorer prognosis in patients with lung cancer. Although HALP may represent a promising and easily accessible prognostic biomarker, its clinical application is currently constrained by substantial cut-off heterogeneity and the lack of externally validated thresholds. Future large-scale prospective studies should establish standardized cut-off values and evaluate HALP as part of integrated prognostic models.</p>

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Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte, and platelet score in patients with lung cancer: a pooled analysis

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Wenxia Wang,
  • Xiangying Chen,
  • Yue Zhou,
  • Wu Guo

摘要

Background

The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be associated with the progression and prognosis of various malignancies. However, its prognostic significance in patients with lung cancer remains controversial.

Methods

We conducted a systematic evaluation to investigate the prognostic value of the HALP score in relation to survival outcomes in patients with lung cancer. A comprehensive search of PubMed, Embase, Web of Science, and the Cochrane Library was performed up to October, 2025, to identify studies assessing the association between HALP and lung cancer prognosis. Pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were calculated for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-free survival (DFS).

Results

A total of 17 studies involving 10,635 lung cancer patients were included. Specifically, 15 studies (10,238 patients) assessed OS, 6 studies (1173 patients) assessed PFS, and 3 studies (362 patients) assessed DFS/RFS. The pooled analysis revealed that a low HALP score was significantly associated with poorer OS (HR 1.82, 95%CI 1.46–2.28, p < 0.001), PFS (HR 1.66, 95%CI 1.17–2.35, p < 0.001), and DFS/RFS (HR 2.78, 95%CI 1.14–6.78, p < 0.001). Subgroup analyses further confirmed that pretreatment HALP was an independent predictor of OS in lung cancer patients.

Conclusions

A lower pretreatment HALP score was significantly associated with poorer prognosis in patients with lung cancer. Although HALP may represent a promising and easily accessible prognostic biomarker, its clinical application is currently constrained by substantial cut-off heterogeneity and the lack of externally validated thresholds. Future large-scale prospective studies should establish standardized cut-off values and evaluate HALP as part of integrated prognostic models.