Multi-parametric magnetic resonance imaging-based radiomics for differentiation of skull base osteomyelitis from...

Skull base osteomyelitis (SBO) and nasopharyngeal carcinoma (NPca) are challenging to differentiate due to overlapping clinical and radiological features. This study aimed to develop and validate a multi-parametric magnetic resonance imaging (MRI)-based radiomics model with high sensitivity, enabling reliable diagnosis of SBO in adult patients presenting with equivocal imaging findings.

This was a retrospective, multicenter study using institutional data. The training cohort, comprising 63 adult patients from two classes (31 SBO, 32 NPca) with MRI data, was used for model development and optimization. An external test set (n = 30; 12 SBO, 18 NPca) obtained from two different clinical centers was used for model performance analysis and generalizability. Lesion segmentation was performed using a manual volumetric technique on three axial MRI sequences (pre-contrast T1-weighted, fat-suppressed T2-weighted, and post-contrast fat-suppressed T1-weighted). Hand-crafted radiomic features (n = 2,553) were extracted using the Pyradiomics library. A multi-step process was used to select the final features, including reproducibility analysis using an intraclass correlation coefficient threshold of 0.9, pairwise Spearman correlation analysis with a threshold of 0.8 to reduce redundancy, and least absolute shrinkage and selection operator regression. The final set of five features were used to train six machine learning models. The models were internally validated using 5-fold cross-validation, and performance was confirmed using the unseen external test set. Traditional statistical tests, including the Mann-Whitney U test and chi-squared test, were used to compare baseline characteristics, with a P value of <0.05 considered significant.

Among the evaluated classifiers, the random forest model demonstrated the best diagnostic performance, yielding the highest area under the curve (AUC) value in the 5-fold cross-validation analysis. In the external test set, the semantic model demonstrated the best diagnostic performance, achieving an AUC of 0.940 [95% confidence interval (CI): 0.857-1.00], followed by the radiomics model (AUC: 0.903, 95% CI: 0.784-1). The apparent diffusion coefficient (ADC)-based model demonstrated limited discriminative ability (AUC: 0.694, 95% CI: 0.497-0.892). The difference between the semantic and radiomics models did not reach statistical significance (P = 0.644), whereas both significantly outperformed the ADC model (P < 0.05).

Radiomics achieved high and consistent performance in distinguishing SBO from advanced NPca. Although expert-based semantic assessment performed slightly better, radiomics provides an objective alternative. ADC-based methods showed limited generalizability due to inter-center variability.

Our study confirms the importance of expert radiologist assessment while demonstrating that radiomics offers a comparably effective and objective decision-support tool. Its ability to provide a consistent, quantitative output is particularly valuable for standardizing the diagnostic approach and empowering less experienced radiologists to make more confident assessments.