Abstract：
OBJECTIVE: This study aimed to develop and validate a predictive nomogram for diagnosing radicular grooves (RG) in maxillary lateral incisors (MLIs), integrating demographic information, anatomical measurements, and Cone Beam Computed Tomography (CBCT) data to diagnose the RG in MLIs based on the clinical observation before resorting to the CBCT scan.
METHODS: A retrospective cohort of orthodontic patients from the School and Hospital of Stomatology, Wuhan University, was analyzed, including demographic characteristics, photographic anatomical assessments, and CBCT diagnoses. The cohort was divided into development and validation groups. Univariate and multivariate logistic regression analyses identified significant predictors of RG, which informed the development of a nomogram. This nomogram\'s performance was validated using receiver operating characteristic analysis.
RESULTS: The study included 381 patients (64.3% female) and evaluated 760 MLIs, with RG present in 26.25% of MLIs. The nomogram incorporated four significant anatomical predictors of RG presence, demonstrating substantial predictive efficacy with an area under the curve of 0.75 in the development cohort and 0.71 in the validation cohort.
CONCLUSIONS: A nomogram for the diagnosis of RG in MLIs was successfully developed. This tool offers a practical checklist of anatomical predictors to improve the diagnostic process in clinical practice.
CONCLUSIONS: The developed nomogram provides a novel, evidence-based tool to enhance the detection and treatment planning of MLIs with RG in diagnostic and therapeutic strategies.
METHODS: A retrospective cohort of orthodontic patients from the School and Hospital of Stomatology, Wuhan University, was analyzed, including demographic characteristics, photographic anatomical assessments, and CBCT diagnoses. The cohort was divided into development and validation groups. Univariate and multivariate logistic regression analyses identified significant predictors of RG, which informed the development of a nomogram. This nomogram\'s performance was validated using receiver operating characteristic analysis.
RESULTS: The study included 381 patients (64.3% female) and evaluated 760 MLIs, with RG present in 26.25% of MLIs. The nomogram incorporated four significant anatomical predictors of RG presence, demonstrating substantial predictive efficacy with an area under the curve of 0.75 in the development cohort and 0.71 in the validation cohort.
CONCLUSIONS: A nomogram for the diagnosis of RG in MLIs was successfully developed. This tool offers a practical checklist of anatomical predictors to improve the diagnostic process in clinical practice.
CONCLUSIONS: The developed nomogram provides a novel, evidence-based tool to enhance the detection and treatment planning of MLIs with RG in diagnostic and therapeutic strategies.
摘要：
目的：本研究旨在开发和验证用于诊断上颌侧切牙（MLIs）的神经根沟（RG）的预测列线图，整合人口统计信息，解剖学测量,和锥形束计算机断层扫描（CBCT）数据，以在诉诸CBCT扫描之前根据临床观察诊断MLI中的RG。
方法：来自口腔医学学校和医院的正畸患者的回顾性队列，武汉大学,被分析,包括人口特征，摄影解剖学评估，和CBCT诊断。该队列分为开发组和验证组。单变量和多变量逻辑回归分析确定了RG的重要预测因子，它为列线图的开发提供了信息。使用接收器工作特性分析验证了该列线图的性能。
结果：该研究包括381名患者（64.3％为女性），评估了760名MLI，在26.25%的MLI中存在RG。列线图包含了RG存在的四个重要的解剖学预测因子，在发展队列中曲线下面积为0.75，在验证队列中曲线下面积为0.71，显示出实质性的预测功效。
结论：成功建立了MLIs中RG诊断的列线图。该工具提供了实用的解剖预测指标清单，以改善临床实践中的诊断过程。
结论：开发的列线图提供了一种新颖的，在诊断和治疗策略中增强RG的MLIs检测和治疗计划的循证工具。
方法：来自口腔医学学校和医院的正畸患者的回顾性队列，武汉大学,被分析,包括人口特征，摄影解剖学评估，和CBCT诊断。该队列分为开发组和验证组。单变量和多变量逻辑回归分析确定了RG的重要预测因子，它为列线图的开发提供了信息。使用接收器工作特性分析验证了该列线图的性能。
结果：该研究包括381名患者（64.3％为女性），评估了760名MLI，在26.25%的MLI中存在RG。列线图包含了RG存在的四个重要的解剖学预测因子，在发展队列中曲线下面积为0.75，在验证队列中曲线下面积为0.71，显示出实质性的预测功效。
结论：成功建立了MLIs中RG诊断的列线图。该工具提供了实用的解剖预测指标清单，以改善临床实践中的诊断过程。
结论：开发的列线图提供了一种新颖的，在诊断和治疗策略中增强RG的MLIs检测和治疗计划的循证工具。
