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Background <p>The pan-immune inflammation value (PIV) reflects the balance between host immune and inflammatory status and is a commonly used indicator for evaluating cancer prognosis. However, to date, no studies have confirmed the prognostic value of pretreatment PIV in patients with locally advanced esophageal squamous cell carcinoma (ESCC) undergoing definitive chemoradiotherapy (dCRT).</p> Methods <p>The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of the PIV for predicting prognosis. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Restricted cubic spline (RCS) curve analysis was used to investigate the relationship between PIV and prognosis. A visual analysis was conducted using the SHAP method. Combined with TNM staging, a comprehensive risk stratification model was constructed through recursive partitioning analysis (RPA). Finally, the model was evaluated through decision curve analysis (DCA) and ROC curve.</p> Results <p>A total of 696 patients were included. The level of PIV before treatment was significantly correlated with a larger primary tumor burden. RCS analysis revealed a non-linear relationship between PIV and survival outcomes. Both univariate and multivariate Cox analyses confirmed that the pre-treatment PIV was an independent prognostic factor for overall survival (OS) and progression-free survival (PFS). Subgroup analysis showed that in all subgroups except those receiving ≥ 3 cycles of chemotherapy, high PIV was an adverse prognostic factor. Based on PIV and TNM stage, a new risk stratification model was constructed through RPA, which classified patients into three risk groups with significantly different OS and PFS. Compared with the traditional TNM stage, this RPA model showed significantly better predictive performance.</p> Conclusion <p>Pretreatment PIV is an independent prognostic biomarker in locally advanced ESCC patients undergoing dCRT. The novel RPA model integrating PIV with TNM staging outperforms traditional staging, offering a refined “anatomical-biological” risk stratification tool to guide personalized treatment.</p>

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Prognostic significance of pretreatment pan-immune inflammation value in esophageal squamous cell carcinoma patients undergoing definitive chemoradiotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Yibin Cai,
  • Ning Wei,
  • Jiarong Zhang,
  • Ruirong Lin,
  • Chao Teng,
  • Qiwei Yao,
  • Zhitao Lin

摘要

Background

The pan-immune inflammation value (PIV) reflects the balance between host immune and inflammatory status and is a commonly used indicator for evaluating cancer prognosis. However, to date, no studies have confirmed the prognostic value of pretreatment PIV in patients with locally advanced esophageal squamous cell carcinoma (ESCC) undergoing definitive chemoradiotherapy (dCRT).

Methods

The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of the PIV for predicting prognosis. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Restricted cubic spline (RCS) curve analysis was used to investigate the relationship between PIV and prognosis. A visual analysis was conducted using the SHAP method. Combined with TNM staging, a comprehensive risk stratification model was constructed through recursive partitioning analysis (RPA). Finally, the model was evaluated through decision curve analysis (DCA) and ROC curve.

Results

A total of 696 patients were included. The level of PIV before treatment was significantly correlated with a larger primary tumor burden. RCS analysis revealed a non-linear relationship between PIV and survival outcomes. Both univariate and multivariate Cox analyses confirmed that the pre-treatment PIV was an independent prognostic factor for overall survival (OS) and progression-free survival (PFS). Subgroup analysis showed that in all subgroups except those receiving ≥ 3 cycles of chemotherapy, high PIV was an adverse prognostic factor. Based on PIV and TNM stage, a new risk stratification model was constructed through RPA, which classified patients into three risk groups with significantly different OS and PFS. Compared with the traditional TNM stage, this RPA model showed significantly better predictive performance.

Conclusion

Pretreatment PIV is an independent prognostic biomarker in locally advanced ESCC patients undergoing dCRT. The novel RPA model integrating PIV with TNM staging outperforms traditional staging, offering a refined “anatomical-biological” risk stratification tool to guide personalized treatment.