Brandon Pitcher1, Ali Alaqla1, Marcel Noujeim2, James A Wealleans3, Georgios Kotsakis4, Vanessa Chrepa5. 1. Department of Endodontics, University of Texas Health Science Center at San Antonio, San Antonio, Texas. 2. Department of Oral and Maxillofacial Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas. 3. Wilford Hall Department of Endodontics, Joint Base San Antonio-Lackland, San Antonio, Texas. 4. Department of Periodontics, University of Washington, Seattle, Washington. 5. Department of Endodontics, University of Texas Health Science Center at San Antonio, San Antonio, Texas. Electronic address: Chrepa@uw.edu.
Abstract
INTRODUCTION: Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. METHODS: Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. RESULTS: Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm3, there was 80% probability of a cyst. If volume was <247 mm3 and root displacement was present, cyst probability was 60% (78% accuracy). CONCLUSIONS: The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.
INTRODUCTION: Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. METHODS: Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. RESULTS: Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm3, there was 80% probability of a cyst. If volume was <247 mm3 and root displacement was present, cyst probability was 60% (78% accuracy). CONCLUSIONS: The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.
Entities:
Keywords:
Binary decision tree; cone-beam computed tomography; cyst screening; differentiation between cysts and granulomas; volumetric analysis
Authors: Roxana M Talpos-Niculescu; Malina Popa; Laura C Rusu; Marius O Pricop; Luminita M Nica; Serban Talpos-Niculescu Journal: Medicina (Kaunas) Date: 2021-05-14 Impact factor: 2.430