A CT-based 3D radiomic signatures combined with clinical airway examinations model for evaluation of mask ventilation in patients undergoing oral and maxillofacial surgeries

Abstract

Objective The objective of this study is to develop a model that incorporates clinical measurements with 3D radiomic signatures extracted from CT images of oral and maxillofacial surgery patients to evaluate mask ventilation. Methods A prospective cohort trial was conducted to enroll patients scheduled for oral and maxillofacial surgery. After obtaining informed consent, clinical measurements and head and neck CT images were collected. The anesthesiologist who managed the airway graded the mask ventilation, with difficult mask ventilation defined as mask ventilation provided without an oral airway or other adjuvant. For radiomics analysis, 3D airway segmentation was extracted and calculated 3D radiomic signatures and corresponding radiological features. Subsequently, features in the clinical measurements model and radiomic signatures model were determined using the least absolute shrinkage and selection operator (LASSO) classifier. A mixed model was developed that incorporated both radiomic signature features and clinical measurement features. Results A total of 716 patients were enrolled in the study. The mixed model combined the five 3D radiomic signatures and six clinical measurements, and was found to have the highest predictive accuracy. In the validation group, the mixed group had an area under the curve (AUC) of 0.851, which was higher than the AUC of 0.812 in the clinical measurements model and 0.827 in the radiomic signatures model. Conclusions This study developed a mixed model that combines 3D radiomic signatures and clinical measurements. Its application in clinical practice can assist in identifying patients at risk of experiencing difficult mask ventilation during oral and maxillofacial surgeries.