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Background <p>Cervical cancer remains a significant health burden worldwide, particularly in patients with markedly elevated pretreatment serum squamous cell carcinoma antigen levels (≥ 10&#xa0;ng/mL), who often have poor outcomes. Accurate prognostic tools for this high-risk population are limited.</p> Methods <p>We conducted a single-center retrospective study including 355 patients with primary cervical squamous cell carcinoma who received radiotherapy between 2020 and 2023. Clinical characteristics and inflammation-related hematological indices, including hemoglobin-to-red cell distribution width ratio and neutrophil-to-lymphocyte ratio, were collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for overall survival. A nomogram incorporating these variables, along with clinical stage and treatment modality, was developed and validated.</p> Results <p>Hemoglobin-to-red cell distribution width ratio, neutrophil-to-lymphocyte ratio, clinical stage, and treatment modality were independent predictors of survival. The nomogram achieved a concordance index of 0.729 in the training cohort and 0.704 in the validation cohort. The area under the time-dependent receiver operating characteristic curves for overall survival at 1, 2, and 3&#xa0;years were 0.76, 0.81, and 0.79 in the training cohort, and 0.80, 0.75, and 0.71 in the validation cohort. Decision curve analysis demonstrated a consistent net clinical benefit, and risk stratification based on total scores effectively distinguished high- and low-risk groups.</p> Conclusions <p>This study developed and validated a clinically useful nomogram integrating inflammation-related hematological indices and clinical parameters to predict survival in high-risk cervical cancer patients. The model demonstrates favorable predictive accuracy and may guide individualized treatment strategies, supporting its potential for clinical application.</p>

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Prognostic prediction in primary cervical squamous cell carcinoma with serum squamous cell carcinoma antigen ≥ 10 ng/mL: development and validation of a nomogram model based on inflammatory biomarkers and clinical factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Yiwei Zhao,
  • Chutong Zhao,
  • Yujie Liu,
  • Yuan Wang,
  • Yunyan Zhang,
  • Sijia Liu

摘要

Background

Cervical cancer remains a significant health burden worldwide, particularly in patients with markedly elevated pretreatment serum squamous cell carcinoma antigen levels (≥ 10 ng/mL), who often have poor outcomes. Accurate prognostic tools for this high-risk population are limited.

Methods

We conducted a single-center retrospective study including 355 patients with primary cervical squamous cell carcinoma who received radiotherapy between 2020 and 2023. Clinical characteristics and inflammation-related hematological indices, including hemoglobin-to-red cell distribution width ratio and neutrophil-to-lymphocyte ratio, were collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for overall survival. A nomogram incorporating these variables, along with clinical stage and treatment modality, was developed and validated.

Results

Hemoglobin-to-red cell distribution width ratio, neutrophil-to-lymphocyte ratio, clinical stage, and treatment modality were independent predictors of survival. The nomogram achieved a concordance index of 0.729 in the training cohort and 0.704 in the validation cohort. The area under the time-dependent receiver operating characteristic curves for overall survival at 1, 2, and 3 years were 0.76, 0.81, and 0.79 in the training cohort, and 0.80, 0.75, and 0.71 in the validation cohort. Decision curve analysis demonstrated a consistent net clinical benefit, and risk stratification based on total scores effectively distinguished high- and low-risk groups.

Conclusions

This study developed and validated a clinically useful nomogram integrating inflammation-related hematological indices and clinical parameters to predict survival in high-risk cervical cancer patients. The model demonstrates favorable predictive accuracy and may guide individualized treatment strategies, supporting its potential for clinical application.