A Novel Volume-based Cone- beam Computed Tomographic Periapical Index

Introduction: The purpose of this study was to evaluate the variations in the volume of periapical lesions scored using a cone-beam computed tomographic periapical index (CBCTPAI) and to develop a new volume-based periapical index. Methods: Cone-beam computed tomographic images were obtained from InteleViewer (Intelerad Medical Systems Incorporated, Montreal, Canada). Teeth with a periapical radiolucency or with a history of endodontic treatment were included in this study. Using 3-dimensional medical imaging processing software (Mimics Research; Materialise NV, Leuven, Belgium), the maximum diameter of 273 periapical lesions and their corresponding CBCTPAI score was determined. The software was then used to determine the volume of the lesions using a semiautomatic segmentation technique. Results: There was a substantial variation in the volume for CBCTPAI scores 3, 4, and 5, which was demonstrated by the variance and range, thus making it difficult to use the current CBCTPAI as a method to predict volume and treatment outcomes. A new index, the cone-beam computed tomographic periapical volume index (CBCTPAVI), was developed using partition classification analysis. The results for the new index demonstrated high levels of sensitivity, specificity, precision, and area under the curve, all at 0.90 or more, except 1 sensitivity for CBCTPAVI 1 at 0.875. Overall, the accurate classification rate was 98.169%, and the root mean square error rate was low at 0.07. Conclusions: The proposed CBCTPAVI will allow clinicians to classify lesions based on their true 3-dimensional size, accurately assess healing of lesions, and predict treatment outcomes. (J Endod 2021;47:1308-1313.)

Automated summary

This study evaluated variations in periapical lesion volumes scored using CBCTPAI and developed a new volume-based periapical index. The new index, CBCTPAVI, demonstrated high levels of sensitivity, specificity, precision, and area under the curve, with an overall accurate classification rate of 98.169% and low root mean square error rate of 0.07, allowing for accurate assessment of healing and treatment outcomes.