Liu Yuebo, Kong Ge, Lu Xiaoping, Meng Fantai, Zhao Jizhi, Guo Chunlan, Wan Kuo
Department of Stomatology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Oral Radiol. 2025 Apr;41(2):197-206. doi: 10.1007/s11282-024-00792-0. Epub 2024 Dec 9.
To explore the effectiveness of radiographic biomarkers on transition area (TA)-the grayscale gradient zone from carious lesion to normal dentine on radiographs-for identifying deep caries/reversible pulpitis and chronic pulpitis via diagnostic model analysis.
This retrospective study included 392 caries cases. Canny edge detection was used to define the TA region. Texture parameters were extracted from the carious lesions (S1) and TA region (S2) by MaZda software on radiographs. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select biomarkers. Diagnostic models were fitted and model performance was furtherly evaluated by internal and external validation, decision curve analysis was applied to evaluate clinical benefits.
TA-based biomarkers (e.g., TA thickness, TA ratio, S2-S(5,-5) contrast and S2-WavEnLL-s-4) were significantly associated with the diagnosis of deep caries/reversible pulpitis versus chronic pulpitis, model performance significantly improved when adding the above biomarkers (likelihood-ratio test; p < 0.05, with an increase of AUC from 0.67 (reference model) to 0.89), and these results were maintained in a small external validation cohort. Clinical benefit was greater with the application of TA-based biomarkers.
TA-based biomarkers are proven to be an effective tool in differentiating deep caries/reversible pulpitis and chronic pulpitis, preoperative diagnosis was improved with the above biomarkers compared to the reference model.
通过诊断模型分析,探讨影像学生物标志物在过渡区(TA)——X线片上从龋损到正常牙本质的灰度梯度区——用于识别深龋/可复性牙髓炎和慢性牙髓炎的有效性。
这项回顾性研究纳入了392例龋病病例。采用Canny边缘检测来定义TA区域。通过Mazda软件从X线片上的龋损(S1)和TA区域(S2)提取纹理参数。使用最小绝对收缩和选择算子(LASSO)回归分析来选择生物标志物。构建诊断模型,并通过内部和外部验证进一步评估模型性能,应用决策曲线分析来评估临床获益。
基于TA的生物标志物(如TA厚度、TA比率、S2 - S(5,-5)对比度和S2 - WavEnLL - s - 4)与深龋/可复性牙髓炎与慢性牙髓炎的诊断显著相关,添加上述生物标志物后模型性能显著改善(似然比检验;p < 0.05,AUC从0.67(参考模型)增加到0.89),并且这些结果在一个小型外部验证队列中得到维持。应用基于TA的生物标志物临床获益更大。
基于TA的生物标志物被证明是区分深龋/可复性牙髓炎和慢性牙髓炎的有效工具,与参考模型相比,上述生物标志物改善了术前诊断。