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Objective <p>This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.</p> Methods <p>We retrospectively analyzed 502 surgically treated cervical cancer cases, incorporating comprehensive clinical parameters, laboratory measurements, and longitudinal follow-up data. Three NII components—advanced lung cancer inflammation index (ALI), neutrophil-albumin ratio (NAR), and prognostic nutritional index (PNI)—were evaluated for their temporal dynamics and relationships with overall survival (OS) and progression-free survival (PFS). Machine learning techniques identified critical predictors for constructing nomograms estimating 3-, 5-, and 10-year survival probabilities.</p> Results <p>Patients maintaining consistently low ALI/PNI values or elevated NAR levels demonstrated significantly poorer outcomes, manifesting earlier disease progression or mortality (log-rank <i>p</i> &lt; 0.001). Based on the key variables screened by the machine learning algorithm, the OS model integrates variables such as NII (PNI), tumor characteristics (federation of gynecology and obstetrics (FIGO) grade, tumor size, lymph node metastasis, cancer antigen 125 (CA125), squamous cell carcinoma (SCC_Ag)), and surgical intervention, while the PFS model incorporates multi-dimensional factors such as PNI, FIGO stage, tumor size, lymph node metastasis, diabetes, surgery, and targeted therapy. Predictive accuracy remained robust across timepoints, with OS model AUCs of 0.894 (95%CI:0.853–0.934), 0.917 (0.88–0.954), and 0.925 (0.883–0.967) for 3-, 5-, and 10-year predictions respectively. Corresponding PFS model AUCs were 0.903 (0.868–0.938), 0.902 (0.865–0.939), and 0.905 (0.862–0.948).</p> Conclusion <p>This study identifies that sustained low ALI/PNI or elevated NAR levels are strongly associated with adverse clinical outcomes in cervical cancer, including earlier progression and mortality.</p>

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Prognostic value of combining nutritional inflammatory index trajectories and tumor characteristics in cervical cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Wen Xing,
  • Zhongjie Wang,
  • Yan Zhu,
  • Ge Ge,
  • Ying Zhang,
  • Qicheng Deng

摘要

Objective

This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.

Methods

We retrospectively analyzed 502 surgically treated cervical cancer cases, incorporating comprehensive clinical parameters, laboratory measurements, and longitudinal follow-up data. Three NII components—advanced lung cancer inflammation index (ALI), neutrophil-albumin ratio (NAR), and prognostic nutritional index (PNI)—were evaluated for their temporal dynamics and relationships with overall survival (OS) and progression-free survival (PFS). Machine learning techniques identified critical predictors for constructing nomograms estimating 3-, 5-, and 10-year survival probabilities.

Results

Patients maintaining consistently low ALI/PNI values or elevated NAR levels demonstrated significantly poorer outcomes, manifesting earlier disease progression or mortality (log-rank p < 0.001). Based on the key variables screened by the machine learning algorithm, the OS model integrates variables such as NII (PNI), tumor characteristics (federation of gynecology and obstetrics (FIGO) grade, tumor size, lymph node metastasis, cancer antigen 125 (CA125), squamous cell carcinoma (SCC_Ag)), and surgical intervention, while the PFS model incorporates multi-dimensional factors such as PNI, FIGO stage, tumor size, lymph node metastasis, diabetes, surgery, and targeted therapy. Predictive accuracy remained robust across timepoints, with OS model AUCs of 0.894 (95%CI:0.853–0.934), 0.917 (0.88–0.954), and 0.925 (0.883–0.967) for 3-, 5-, and 10-year predictions respectively. Corresponding PFS model AUCs were 0.903 (0.868–0.938), 0.902 (0.865–0.939), and 0.905 (0.862–0.948).

Conclusion

This study identifies that sustained low ALI/PNI or elevated NAR levels are strongly associated with adverse clinical outcomes in cervical cancer, including earlier progression and mortality.