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Background and objective <p>This study aims to explore the association between the advanced lung cancer inflammation index (ALI), the adjusted Charlson Comorbidity Index (ACCI), and other relevant clinical factors and the prognosis of patients with cervical squamous cell carcinoma (CSCC) who undergoing concurrent chemoradiotherapy, and to construct a corresponding prognostic model.</p> Methods <p>A total of 243 patients with CSCC who undergoing concurrent chemoradiotherapy between January 2017 and December 2023 were included in this study. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors influencing progression-free survival (PFS) and overall survival (OS). These independent prognostic factors were subsequently utilized to construct two nomograms, which were then subjected to a comprehensive series of validations. Ultimately, a risk stratification framework was developed to evaluate the prognostic outcomes of patients across varying risk categories.</p> Results <p>ALI, ACCI, American Joint Committee on Cancer (AJCC) stage, and tumor volume were identified as independent predictors of PFS and OS (all <i>P</i> &lt; 0.05). Based on these independent clinical factors, we developed two distinct nomograms for the prediction of progression-free survival (PFS) and overall survival (OS), respectively. In the training cohort, the C-indexes of the model for PFS and OS were 0.743 and 0.741, respectively; while the corresponding C-indexes were 0.735 and 0.728 in the validation cohort. Following an extensive series of validations, the newly developed nomogram models demonstrated superior performance compared to the traditional AJCC staging system. Based on the total risk points derived from the nomograms, we stratified all patients into three risk subgroups: high-risk, medium-risk, and low-risk. Patients in three distinct risk subgroups exhibited significantly different survival outcomes.</p> Conclusion <p>ALI has significant value for predicting PFS and OS of CSCC patients who have undergone concurrent chemoradiotherapy. The newly developed nomogram models based on ALI demonstrates robust performance and offers a valuable reference for personalized treatment strategies.</p>

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Nomogram based on the advanced lung cancer inflammation index and other relevant clinical factors for patients with cervical squamous cell carcinoma undergoing concurrent chemoradiotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Xiao-Chun Wang,
  • Xue-Lian Xu,
  • Shou-Yu Wang,
  • Hao Cheng,
  • Peng-Fei Yan,
  • Ming-Yu Yang,
  • Ke-Chen

摘要

Background and objective

This study aims to explore the association between the advanced lung cancer inflammation index (ALI), the adjusted Charlson Comorbidity Index (ACCI), and other relevant clinical factors and the prognosis of patients with cervical squamous cell carcinoma (CSCC) who undergoing concurrent chemoradiotherapy, and to construct a corresponding prognostic model.

Methods

A total of 243 patients with CSCC who undergoing concurrent chemoradiotherapy between January 2017 and December 2023 were included in this study. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors influencing progression-free survival (PFS) and overall survival (OS). These independent prognostic factors were subsequently utilized to construct two nomograms, which were then subjected to a comprehensive series of validations. Ultimately, a risk stratification framework was developed to evaluate the prognostic outcomes of patients across varying risk categories.

Results

ALI, ACCI, American Joint Committee on Cancer (AJCC) stage, and tumor volume were identified as independent predictors of PFS and OS (all P < 0.05). Based on these independent clinical factors, we developed two distinct nomograms for the prediction of progression-free survival (PFS) and overall survival (OS), respectively. In the training cohort, the C-indexes of the model for PFS and OS were 0.743 and 0.741, respectively; while the corresponding C-indexes were 0.735 and 0.728 in the validation cohort. Following an extensive series of validations, the newly developed nomogram models demonstrated superior performance compared to the traditional AJCC staging system. Based on the total risk points derived from the nomograms, we stratified all patients into three risk subgroups: high-risk, medium-risk, and low-risk. Patients in three distinct risk subgroups exhibited significantly different survival outcomes.

Conclusion

ALI has significant value for predicting PFS and OS of CSCC patients who have undergone concurrent chemoradiotherapy. The newly developed nomogram models based on ALI demonstrates robust performance and offers a valuable reference for personalized treatment strategies.