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Background <p>Differentiating between benign and malignant biliary strictures is crucial yet challenging. The role of nutritional indicators in this differentiation process remains unclear. This study aimed to explore the potential of nutritional indicators for improving the diagnostic accuracy of biliary strictures.</p> Methods <p>This prospective study recruited patients aged 18–80&#xa0;years with suspected biliary strictures. The final diagnosis was based on pathological examination or long-term follow-up. In addition to common clinical variables, nutritional indicators, including the Global Leadership Initiative on Malnutrition (GLIM), Mini-Nutritional Assessment (MNA), Nutritional Risk Screening 2002 (NRS2002), and anthropometric measurements, were collected. Univariate and multivariate logistic regression, along with the Boruta algorithm, were used for variable selection. Multiple machine learning models were constructed. The primary outcome was defined as the differentiation of benign from malignant biliary strictures. Model performance was evaluated through metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.</p> Results <p>A total of 264 patients (106 with benign and 158 with malignant biliary strictures) were included. Through univariate and multivariate logistic regression, 7 variables related to malignant strictures were identified, including nutritional parameters such as the MNA classification. Among all 7 machine learning models, the multivariate logistic regression model demonstrated the optimal performance, with an AUC of 0.91 (95% CI 0.86–0.95), accuracy of 0.83 (95% CI 0.68–0.93), and specificity of 0.91 (95% CI 0.81–1.00). The integration of nutritional indicators significantly enhanced the diagnostic performance. The AUC increased from 0.87 (95% CI 0.77–0.96) to 0.91 (95% CI 0.86–0.95), and the accuracy increased from 0.69 (95% CI 0.56–0.80) to 0.83 (95% CI 0.68–0.93).</p> Conclusions <p>Incorporating nutritional indicators into the diagnostic model can improve the accuracy of differentiating between benign and malignant biliary strictures. This provides a non-invasive and more comprehensive diagnostic approach, which may guide better treatment decisions.</p>

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Nutritional risk assessment as a novel tool for differentiating malignant from benign biliary strictures: a prospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Jialu Jiang,
  • Chenxi Kang,
  • Jing Li,
  • Chao Zhang,
  • Yaling Liu,
  • Junjun Ye,
  • Xiaoyu Kang,
  • Gui Ren,
  • Linhui Zhang,
  • Hui Luo,
  • Shuhui Liang,
  • Yanglin Pan

摘要

Background

Differentiating between benign and malignant biliary strictures is crucial yet challenging. The role of nutritional indicators in this differentiation process remains unclear. This study aimed to explore the potential of nutritional indicators for improving the diagnostic accuracy of biliary strictures.

Methods

This prospective study recruited patients aged 18–80 years with suspected biliary strictures. The final diagnosis was based on pathological examination or long-term follow-up. In addition to common clinical variables, nutritional indicators, including the Global Leadership Initiative on Malnutrition (GLIM), Mini-Nutritional Assessment (MNA), Nutritional Risk Screening 2002 (NRS2002), and anthropometric measurements, were collected. Univariate and multivariate logistic regression, along with the Boruta algorithm, were used for variable selection. Multiple machine learning models were constructed. The primary outcome was defined as the differentiation of benign from malignant biliary strictures. Model performance was evaluated through metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

A total of 264 patients (106 with benign and 158 with malignant biliary strictures) were included. Through univariate and multivariate logistic regression, 7 variables related to malignant strictures were identified, including nutritional parameters such as the MNA classification. Among all 7 machine learning models, the multivariate logistic regression model demonstrated the optimal performance, with an AUC of 0.91 (95% CI 0.86–0.95), accuracy of 0.83 (95% CI 0.68–0.93), and specificity of 0.91 (95% CI 0.81–1.00). The integration of nutritional indicators significantly enhanced the diagnostic performance. The AUC increased from 0.87 (95% CI 0.77–0.96) to 0.91 (95% CI 0.86–0.95), and the accuracy increased from 0.69 (95% CI 0.56–0.80) to 0.83 (95% CI 0.68–0.93).

Conclusions

Incorporating nutritional indicators into the diagnostic model can improve the accuracy of differentiating between benign and malignant biliary strictures. This provides a non-invasive and more comprehensive diagnostic approach, which may guide better treatment decisions.