Ali Alqerban1, Reinhilde Jacobs2, Steffen Fieuws3, Guy Willems4. 1. *Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia, ****Department of Oral Health Sciences, KU Leuven and Dentistry, University Hospitals Leuven, Belgium. 2. **Department of Imaging and Pathology/OMFS-IMPATH Research Group, KU Leuven. 3. ***Department of Public Health, KU Leuven and Universiteit Hasselt. 4. ****Department of Oral Health Sciences, KU Leuven and Dentistry, University Hospitals Leuven, Belgium guy.willems@med.kuleuven.be.
Abstract
OBJECTIVES: The aim was to identify a prediction model for root resorption (RR) caused by impacted canines based on radiographic variables assessed on 2D panoramic radiographs with the intention to reduce the need for additional cone beam computed tomography (CBCT) imaging. MATERIALS AND METHODS: Three hundred and six patients (188 female, 118 male; mean age, 14.7 years; standard deviation, 5.6; range, 8.4-47.2 years) were included in the study. In total, 406 impacted maxillary canines were studied, from 206 patients with unilateral impaction and from 100 patients with bilateral impaction. Initial 2D panoramic radiography was available, and 3D CBCT imaging was obtained upon clinical indication. The generated radiographic variables and specific features investigated were collected on 2D panoramic imaging and were correlated to the presence/absence of RR detected on CBCT. A validation sample consisting of 55 canines from 45 patients with maxillary canine impactions was collected to validate the outcome of the present study. RESULTS: The incidence of RR of the adjacent teeth was 33.8%. A prediction model using panoramic images for the possible presence of RR was established [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.69-0.79] and validated by applying leave-one-out cross-validation (AUC = 0.71, 95% CI: 0.66-0.77). For the subgroup of presence of severe RR the discriminative ability increased to 0.80. In this prediction model, patient gender, canine apex, vertical canine crown position, and canine magnification were the strongest predictors for RR. CONCLUSIONS: The final prediction model for RR based on available panoramic radiographs could be a helpful tool in justifying the need of additional CBCT examination. Published by Oxford University Press on behalf of the European Orthodontic Society 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.
OBJECTIVES: The aim was to identify a prediction model for root resorption (RR) caused by impacted canines based on radiographic variables assessed on 2D panoramic radiographs with the intention to reduce the need for additional cone beam computed tomography (CBCT) imaging. MATERIALS AND METHODS: Three hundred and six patients (188 female, 118 male; mean age, 14.7 years; standard deviation, 5.6; range, 8.4-47.2 years) were included in the study. In total, 406 impacted maxillary canines were studied, from 206 patients with unilateral impaction and from 100 patients with bilateral impaction. Initial 2D panoramic radiography was available, and 3D CBCT imaging was obtained upon clinical indication. The generated radiographic variables and specific features investigated were collected on 2D panoramic imaging and were correlated to the presence/absence of RR detected on CBCT. A validation sample consisting of 55 canines from 45 patients with maxillary canine impactions was collected to validate the outcome of the present study. RESULTS: The incidence of RR of the adjacent teeth was 33.8%. A prediction model using panoramic images for the possible presence of RR was established [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.69-0.79] and validated by applying leave-one-out cross-validation (AUC = 0.71, 95% CI: 0.66-0.77). For the subgroup of presence of severe RR the discriminative ability increased to 0.80. In this prediction model, patient gender, canine apex, vertical canine crown position, and canine magnification were the strongest predictors for RR. CONCLUSIONS: The final prediction model for RR based on available panoramic radiographs could be a helpful tool in justifying the need of additional CBCT examination. Published by Oxford University Press on behalf of the European Orthodontic Society 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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