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Objective <p>To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance.</p> Methods <p>Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators were extracted. LASSO regression identified 10 core risk factors, which were further analyzed using binary logistic regression to develop a nomogram model in R. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA).</p> Results <p>The model showed excellent performance, with a C-index of 0.887. The key factors included sex, coagulation, blood cell indexes, and blood lipid levels. The ROC areas were 0.892 (quantitative) and 0.853 (qualitative), confirming model reliability.</p> Conclusion <p>A new nomogram model for assessing GC risk in CAG patients was successfully developed. However, due to data collection and time limitations, future studies should expand the sample size, perfect the validation process, and optimize the model to achieve more accurate risk prediction.</p>

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Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Jia-Yi Zhang,
  • Ding Li,
  • Guo-Jie Hu

摘要

Objective

To construct a machine learning (ML) model to predict the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC), given its precancerous significance.

Methods

Using medical records from the Affiliated Hospital of Qingdao University, common laboratory indicators were extracted. LASSO regression identified 10 core risk factors, which were further analyzed using binary logistic regression to develop a nomogram model in R. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results

The model showed excellent performance, with a C-index of 0.887. The key factors included sex, coagulation, blood cell indexes, and blood lipid levels. The ROC areas were 0.892 (quantitative) and 0.853 (qualitative), confirming model reliability.

Conclusion

A new nomogram model for assessing GC risk in CAG patients was successfully developed. However, due to data collection and time limitations, future studies should expand the sample size, perfect the validation process, and optimize the model to achieve more accurate risk prediction.