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Objective <p>This study aimed to investigate the risk factors for colorectal sessile serrated lesions and to develop a nomogram prediction model that can assist in differentiating sessile serrated lesion from hyperplastic polyps during endoscopic and pathological diagnosis.</p> Methods <p>A total of 1628 eligible patients were included in this study. After collecting clinical information and data, they were randomly allocated into training and validation datasets at an 8:2 ratio. Univariate and multivariate logistic regression analyses were employed to identify risk factors and construct a nomogram prediction model. The model’s performance was comprehensively evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.</p> Results <p>Multivariate logistic regression analysis indicated that age, hyperlipidemia, diarrhea, <i>Helicobacter pylori</i> infection, gastric neoplasm, polyp size, location, and laterally spreading morphology were all independent predictors of colorectal sessile serrated lesion. A nomogram prediction model was constructed and validated based on these variables. The predictive model demonstrated excellent discriminatory performance in both the training set and the independent validation set, with areas under the curve of 0.876 and 0.880, respectively. At the optimal diagnostic threshold of 0.687, the model achieved a specificity of 92.6% and a sensitivity of 61.7%, enabling effective identification with high specificity. The model showed good calibration, and decision curve analysis confirmed that it provides clear net clinical benefit across a wide range of threshold probabilities, indicating that this nomogram model has promising potential for clinical application.</p> Conclusion <p>This nomogram prediction model integrated eight independent predictors and demonstrated effective performance in differentiating colorectal sessile serrated lesions from hyperplastic polyps, showing favourable clinical utility and generalisation potential. It contributes to improving early detection rates, assists in optimising endoscopic resection strategies and provides a quantitative reference for pathological diagnosis, thereby facilitating more precise clinical intervention.</p>

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Distinguishing colorectal sessile serrated lesions from hyperplastic polyps: development of a prediction model based on logistic regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Dian Zhang,
  • Xiao Tan,
  • Weiling Hu

摘要

Objective

This study aimed to investigate the risk factors for colorectal sessile serrated lesions and to develop a nomogram prediction model that can assist in differentiating sessile serrated lesion from hyperplastic polyps during endoscopic and pathological diagnosis.

Methods

A total of 1628 eligible patients were included in this study. After collecting clinical information and data, they were randomly allocated into training and validation datasets at an 8:2 ratio. Univariate and multivariate logistic regression analyses were employed to identify risk factors and construct a nomogram prediction model. The model’s performance was comprehensively evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.

Results

Multivariate logistic regression analysis indicated that age, hyperlipidemia, diarrhea, Helicobacter pylori infection, gastric neoplasm, polyp size, location, and laterally spreading morphology were all independent predictors of colorectal sessile serrated lesion. A nomogram prediction model was constructed and validated based on these variables. The predictive model demonstrated excellent discriminatory performance in both the training set and the independent validation set, with areas under the curve of 0.876 and 0.880, respectively. At the optimal diagnostic threshold of 0.687, the model achieved a specificity of 92.6% and a sensitivity of 61.7%, enabling effective identification with high specificity. The model showed good calibration, and decision curve analysis confirmed that it provides clear net clinical benefit across a wide range of threshold probabilities, indicating that this nomogram model has promising potential for clinical application.

Conclusion

This nomogram prediction model integrated eight independent predictors and demonstrated effective performance in differentiating colorectal sessile serrated lesions from hyperplastic polyps, showing favourable clinical utility and generalisation potential. It contributes to improving early detection rates, assists in optimising endoscopic resection strategies and provides a quantitative reference for pathological diagnosis, thereby facilitating more precise clinical intervention.