Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.

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Abstract

Differentiating fungal from chronic sinusitis remains a diagnostic challenge due to overlapping symptoms. This study aims to enhance diagnostic accuracy for fungal sinusitis by integrating radiomic analysis with machine learning. Data from 106 fungal and 146 chronic sinusitis patients, confirmed by surgical pathology at the Integrated Traditional Chinese and Western Medicine Hospital of Wenzhou Medical University from January 2022 to December 2025, were analyzed. Radiomic features from CT scans were extracted and reduced to 19 key indicators using 3DSlicer, Minimum Redundancy Maximum Relevance (mRMR), and lasso regression. A comparative analysis of logistic regression, support vector machine, and random forest models selected the optimal model based on AUC performance. Using mRMR followed by LASSO regression, 19 key radiomic features were selected to distinguish fungal sinusitis from chronic sinusitis. Among logistic regression, SVM, and random forest, the random forest model performed best, achieving a training AUC of 96.04% and a test AUC of 94.91%, with corresponding accuracies of 92.51% and 91.30%. Calibration curves confirmed excellent agreement between predicted and actual outcomes, demonstrating strong diagnostic reliability. Calibration curves showed strong agreement between predicted probabilities and actual outcomes. Employing radiomic features and machine learning, specifically a refined random forest model, significantly improves fungal sinusitis diagnosis accuracy. This approach promises to minimize diagnostic errors and enhance therapeutic decisions, marking a significant advance in precise diagnostics for fungal sinusitis. However, external validation from independent institutions was not performed, representing a key limitation of the study. Not applicable.

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