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Appendicitis-II: Diagnostic Studies and Management01:29

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Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
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Summary

Routine hematologic biomarkers and machine learning models effectively differentiate between LH and AA. This non-invasive approach shows high diagnostic accuracy, identifying key markers like LMR and NLR.

Keywords:
Acute appendicitisInflammatory biomarkersLymphoid hyperplasiaMachine learningSHAP (model interpretability)

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Area of Science:

  • Hematology
  • Medical Informatics
  • Machine Learning

Background:

  • Assessing routine laboratory biomarkers for improved differentiation between liver hemangioma (LH) and adrenal adenoma (AA).
  • Developing machine learning (ML) models to enhance diagnostic capabilities.

Purpose of the Study:

  • To evaluate the diagnostic performance of routine laboratory biomarkers.
  • To create and compare ML models for differentiating LH from AA.
  • To identify key hematologic parameters influencing diagnostic predictions.

Main Methods:

  • Retrospective analysis of 873 patients (209 LH; 664 AA).
  • Utilized laboratory parameters: CRP, WBC, neutrophils, lymphocytes, monocytes, NLR, LMR, PLR, PIV.
  • Developed and evaluated logistic regression, Naive Bayes, neural network, and gradient boosting models using AUC, accuracy, precision, recall, and F1 score.

Main Results:

  • Significant differences in all biomarkers between LH and AA groups (p < 0.001).
  • AA patients showed higher CRP, WBC, neutrophils, NLR, PLR, PIV; LH patients had higher LMR.
  • Logistic regression model achieved the highest performance (AUC 0.918, accuracy 0.869), closely followed by Naive Bayes and neural network.
  • SHAP analysis identified LMR and NLR as most influential features.

Conclusions:

  • Routine hematologic biomarkers combined with ML modeling offer a robust, non-invasive diagnostic tool.
  • This approach significantly improves the differentiation between LH and AA.
  • LMR and NLR are critical indicators in distinguishing between these conditions.