Scholarlink

Background <p>Lung cancer remains the leading cause of cancer-related mortality globally, with late-stage diagnosis contributing to poor outcomes. Non-invasive biomarkers capable of complementing existing screening modalities are urgently needed to improve early detection.</p> Methods <p>This retrospective study developed and validated a diagnostic model using serum tumor markers and inflammation-related biomarkers. The training cohort included 110 treatment-naive lung cancer patients and 107 healthy controls, while an external validation cohort comprised 44 lung cancer patients and 52 controls. Compare the differences in tumor markers, cytokines, and indices derived from whole blood cell counts between the lung cancer group and the healthy control group. Logistic regression analysis was used to identify independent risk factors for lung cancer and to construct a clinical prediction model. The model’s discrimination, calibration, and clinical applicability were assessed using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow test, and decision curve analysis (DCA), among other methods.</p> Results <p>The predictive model demonstrated high diagnostic accuracy in the training cohort, with an AUC of 0.972 (sensitivity 89.2%, specificity 99.1%). External validation showed an AUC of 0.897 (sensitivity 72.7%, specificity 98.1%), confirming its good generalizability. Notably, Pro-Gastrin Releasing Peptide(ProGRP)and inflammatory markers were independently associated with the risk of lung cancer (<i>P</i> &lt; 0.05). DCA analysis indicated that the model has significant clinical utility across a wide range of probability thresholds (0–80%).</p> Conclusion <p>This study presents a high-performance, non-invasive diagnostic model integrating ProGRP and systemic inflammatory biomarkers for lung cancer detection. The combined model outperforms individual biomarkers and may enhance early lung cancer screening, particularly in settings where imaging modalities are limited. Further multi-center prospective studies are warranted to validate its clinical applicability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel diagnostic model combining ProGRP and inflammatory biomarkers for early detection of lung cancer: development and validation in treatment-naive cohorts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

查看全文
  • Jin Ma,
  • Shumin Zhu,
  • Xinyuan Li,
  • Shichao Gao,
  • Yulan Geng

摘要

Background

Lung cancer remains the leading cause of cancer-related mortality globally, with late-stage diagnosis contributing to poor outcomes. Non-invasive biomarkers capable of complementing existing screening modalities are urgently needed to improve early detection.

Methods

This retrospective study developed and validated a diagnostic model using serum tumor markers and inflammation-related biomarkers. The training cohort included 110 treatment-naive lung cancer patients and 107 healthy controls, while an external validation cohort comprised 44 lung cancer patients and 52 controls. Compare the differences in tumor markers, cytokines, and indices derived from whole blood cell counts between the lung cancer group and the healthy control group. Logistic regression analysis was used to identify independent risk factors for lung cancer and to construct a clinical prediction model. The model’s discrimination, calibration, and clinical applicability were assessed using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow test, and decision curve analysis (DCA), among other methods.

Results

The predictive model demonstrated high diagnostic accuracy in the training cohort, with an AUC of 0.972 (sensitivity 89.2%, specificity 99.1%). External validation showed an AUC of 0.897 (sensitivity 72.7%, specificity 98.1%), confirming its good generalizability. Notably, Pro-Gastrin Releasing Peptide(ProGRP)and inflammatory markers were independently associated with the risk of lung cancer (P < 0.05). DCA analysis indicated that the model has significant clinical utility across a wide range of probability thresholds (0–80%).

Conclusion

This study presents a high-performance, non-invasive diagnostic model integrating ProGRP and systemic inflammatory biomarkers for lung cancer detection. The combined model outperforms individual biomarkers and may enhance early lung cancer screening, particularly in settings where imaging modalities are limited. Further multi-center prospective studies are warranted to validate its clinical applicability.