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Objective <p>This study aimed to develop and validate a predictive model for pathological complete response (pCR) following neoadjuvant therapy in esophageal squamous cell carcinoma (ESCC), focusing on Nutrition-Inflammation Index (NII) and its potential nonlinear relationship with pCR.</p> Methods <p>A single-center retrospective cohort of 363 ESCC patients receiving neoadjuvant therapy followed by esophagectomy was analyzed. We employed restricted cubic splines within multivariable logistic regression to characterize the relationship between log-transformed Nutrition-Inflammation Index (logNII) and pCR. The model’s performance was rigorously assessed by its discriminative ability (Area Under the Curve, AUC), calibration, and clinical utility using bootstrap validation and decision curve analysis (DCA).</p> Results <p>The logNII demonstrated a significant, independent, and nonlinear association with pCR after adjusting for key clinical covariates. The final predictive model, which incorporated logNII and clinical variables, achieved an AUC of 0.816 (95% CI: 0.770–0.863). DCA confirmed the model provided significant net clinical benefit across a wide range of threshold probabilities, highlighting its potential for clinical decision-making.</p> Conclusion <p>We established logNII as a robust, independent, and nonlinear predictor of pCR in ESCC. The developed model demonstrates excellent predictive performance and clinical utility, offering a valuable tool for personalizing treatment strategies in the neoadjuvant setting.</p>

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A logNII-based model for predicting pathological complete response following neoadjuvant therapy in esophageal squamous cell carcinoma: a retrospective single-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Min Zhang,
  • Jiahao Huang,
  • Chunyan Wang,
  • Bin Yang,
  • Tao Lu

摘要

Objective

This study aimed to develop and validate a predictive model for pathological complete response (pCR) following neoadjuvant therapy in esophageal squamous cell carcinoma (ESCC), focusing on Nutrition-Inflammation Index (NII) and its potential nonlinear relationship with pCR.

Methods

A single-center retrospective cohort of 363 ESCC patients receiving neoadjuvant therapy followed by esophagectomy was analyzed. We employed restricted cubic splines within multivariable logistic regression to characterize the relationship between log-transformed Nutrition-Inflammation Index (logNII) and pCR. The model’s performance was rigorously assessed by its discriminative ability (Area Under the Curve, AUC), calibration, and clinical utility using bootstrap validation and decision curve analysis (DCA).

Results

The logNII demonstrated a significant, independent, and nonlinear association with pCR after adjusting for key clinical covariates. The final predictive model, which incorporated logNII and clinical variables, achieved an AUC of 0.816 (95% CI: 0.770–0.863). DCA confirmed the model provided significant net clinical benefit across a wide range of threshold probabilities, highlighting its potential for clinical decision-making.

Conclusion

We established logNII as a robust, independent, and nonlinear predictor of pCR in ESCC. The developed model demonstrates excellent predictive performance and clinical utility, offering a valuable tool for personalizing treatment strategies in the neoadjuvant setting.