Predicting drainage success of peritonsillar abscesses: a radiomics-based machine learning approach using
İzzet Selçuk Parlak1, Bökebatur Ahmet Raşit Mendi2, Alperen Özkan1
1Bilkent City Hospital, Clinic of Radiology, Ankara, Türkiye.
Purpose:
To develop and validate radiomics-based machine learning models combined with clinical parameters derived from venous phase contrast-enhanced computed tomography (CECT) for predicting drainage success in patients with peritonsillar abscess (PTA), aiming to reduce unnecessary invasive procedures.
Methods:
This retrospective study included 94 adult patients with PTA who underwent venous phase CECT followed by incision and drainage within 24 hours. Patients were categorized into drainage (n = 52) and non-drainage (n = 42) groups based on procedural outcomes. Clinical parameters (age, sex, trismus, and uvula deviation) were integrated with 107 extracted radiomics features. Three-dimensional manual segmentation was performed using 3D Slicer. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. Four machine learning algorithms-support vector machine (SVM), random forest classifier (RFC), decision tree, and extreme gradient boosting (XGBoost)-were developed, and a combined clinical-radiomics model was constructed. Model performance was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
Results:
The LASSO selected 10 discriminative features (optimal λ: 0.043). Inter- and intraobserver reproducibility demonstrated good agreement (intraclass correlation coefficient ≥ 0.75; Spearman's ρ > 0.8). Among the radiomics-only models, XGBoost achieved the highest diagnostic performance (AUC: 0.919). The combined clinical-radiomics model further improved diagnostic performance, reaching an AUC of 0.934, with a sensitivity of 90.2% and a specificity of 94.1%. The SVM yielded an AUC of 0.899, whereas the decision tree and RFC demonstrated AUCs of 0.887 and 0.850, respectively. Notably, the high specificity of the combined model suggests strong potential for identifying non-drainable collections.
Conclusion:
Integrating clinical parameters with radiomics-based machine learning, particularly the combined clinical-radiomics model, can accurately predict drainage success in PTA using pre-procedural CECT images. This quantitative approach may improve patient selection for invasive interventions.
Clinical Significance:
Pre-procedural identification of non-drainable collections may help avoid unnecessary drainage attempts, reduce procedure-related morbidity, and support more informed clinical decision-making in the management of PTA.
