ArticleBMC medical imaging2026
Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferentiating fungal from chronic sinusitis remains a diagnostic challenge due to overlapping symptoms.
objectivesThis study aims to enhance diagnostic accuracy for fungal sinusitis by integrating radiomic analysis with machine learning.
methodsData 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.
resultsUsing 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.
conclusionsEmploying 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. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.