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Background <p>Fournier Gangrene (FG) and Generalized Perianal Abscess (GPA) have similar clinical features. But FG has a high mortality and disability rate and needs to be identified and treated as early as possible. This study utilized machine learning methods to integrate clinical and metabolic features to promote early diagnosis of FG.</p> Methods <p>Serological characteristics were screened for patients with FG (<i>n</i> = 20) and GPA (<i>n</i> = 16). The metabolomic changes of FG were described based on untargeted metabolomics. We used machine learning tools to combine demographic data, clinical serology, and metabolomics data to establish disease-specific boundary points.</p> Results <p>There were significant differences in the serum metabolic profiles between the FG and GPA groups. 118 different metabolites were detected, mainly fatty acids. Based on machine learning integration of metabolic and clinical features, a differential diagnosis combination of Myo-inositol (MI), Procalcitonin (PCT) and Bistris was established for early identification and diagnosis of FG. The diagnostic performance was evaluated using GBDT, SVM, and LR algorithms, demonstrating robust discriminative ability (AUC: 0.80, 0.82, and 0.95; sensitivity: 0.90, 0.92, and 1.00). In addition, we identified 14 differential metabolic pathways. The activation of Necroptosis may lead to the occurrence of explosive perianal and perineal infections.</p> Conclusion <p>Our findings provide a biomarker combination for early diagnosis of FG in clinical applications. On the other hand, it provides important insights into the pathological mechanism differences between FG and GPA.</p>

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Early identification and diagnosis of fournier gangrene: a machine learning approach integrating serological characterization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Jiayuan Zhang,
  • Jingen Lu,
  • Changfang Xiao,
  • Jingwen Wu,
  • Chen Wang,
  • Yibo Yao

摘要

Background

Fournier Gangrene (FG) and Generalized Perianal Abscess (GPA) have similar clinical features. But FG has a high mortality and disability rate and needs to be identified and treated as early as possible. This study utilized machine learning methods to integrate clinical and metabolic features to promote early diagnosis of FG.

Methods

Serological characteristics were screened for patients with FG (n = 20) and GPA (n = 16). The metabolomic changes of FG were described based on untargeted metabolomics. We used machine learning tools to combine demographic data, clinical serology, and metabolomics data to establish disease-specific boundary points.

Results

There were significant differences in the serum metabolic profiles between the FG and GPA groups. 118 different metabolites were detected, mainly fatty acids. Based on machine learning integration of metabolic and clinical features, a differential diagnosis combination of Myo-inositol (MI), Procalcitonin (PCT) and Bistris was established for early identification and diagnosis of FG. The diagnostic performance was evaluated using GBDT, SVM, and LR algorithms, demonstrating robust discriminative ability (AUC: 0.80, 0.82, and 0.95; sensitivity: 0.90, 0.92, and 1.00). In addition, we identified 14 differential metabolic pathways. The activation of Necroptosis may lead to the occurrence of explosive perianal and perineal infections.

Conclusion

Our findings provide a biomarker combination for early diagnosis of FG in clinical applications. On the other hand, it provides important insights into the pathological mechanism differences between FG and GPA.