上海口腔医学

上海口腔医学 ›› 2026, Vol. 35 ›› Issue (2): 148-153.doi: 10.19439/j.sjos.2026.02.006

• 论著 • 上一篇    下一篇

基于髁突CBCT特征的随机森林算法对特发性髁突吸收患者分期的诊断价值

马宇璇1,2, 聂蓉蓉3, 张力3, 孙卫斌4, 朱锋1   

  1. 1.南京大学医学院附属口腔医院,南京市口腔医院,南京大学口腔医学研究所 口腔颌面外科,江苏 南京 210008;
    2.南通市口腔医院 综合科,江苏 南通 226001;
    3.南京大学医学院附属口腔医院,南京市口腔医院,南京大学口腔医学研究所 修复科,4.牙周病科,江苏 南京 210008
  • 收稿日期:2025-10-30 修回日期:2025-12-16 出版日期:2026-04-25 发布日期:2026-04-27
  • 通讯作者: 朱锋,E-mail：drzf@sina.com
  • 作者简介:马宇璇（1996—）,女,学士,住院医师,E-mail：myx11199@163.com
  • 基金资助:
    2024年江苏省干部保健科研课题（BJ24040）; 南京市口腔医院高水平医院建设科研项目（0224C032）

Diagnostic value of random forest algorithm based on condylar CBCT features for the staging of patients with idiopathic condylar resorption

Ma Yuxuan1,2, Nie Rongrong3, Zhang Li3, Sun Weibin4, Zhu Feng1   

  1. 1. Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University; Nanjing Stomatological Hospital; Institute of Stomatology, Nanjing University. Nanjing 210008;
    2. Department of General Dentistry, Nantong Stomatological Hospital. Nantong 226001;
    3. Department of Prosthodontics, 4. Department of Periodontology, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University; Nanjing Stomatological Hospital; Institute of Stomatology, Nanjing University. Nanjing 210008, Jiangsu Province, China
  • Received:2025-10-30 Revised:2025-12-16 Online:2026-04-25 Published:2026-04-27

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摘要： 目的:探讨基于髁突CBCT特征的随机森林算法对特发性髁突吸收（idiopathic condylar resorption, ICR）患者分期的诊断价值。方法:选择2016年1月—2024年12月南京市口腔医院收治的ICR患者120例,分为静止期组（70例）和进展期组（50例）,选择同期体检的健康人（40例）作为对照组。测量髁突长度、内1/3宽度、中 1/3宽度、外1/3宽度、髁突高度、下颌支高度、髁表面角、髁轴角、髁体积、髁表面积和髁截面积等CBCT特征指标。结果:各组性别、年龄、身高和病程相比无显著差异（P>0.05）,CBCT特征指标差异显著（P<0.05）。与静止期组相比,进展期组的髁突长度、髁突高度、下颌支高度、髁体积、髁表面积和髁截面积显著减少（P<0.05）。以ICR患者处于静止期或进展期为因变量,将单因素分析差异显著的变量纳入随机森林（random forest, RF）算法,重要性排序自高至低依次为髁体积、髁表面积、髁突长度、下颌支高度、髁截面积、髁突高度。将上述CBCT特征进行逐步RF,特征数为5时,袋外数据的误差率最低,重要性排序自高至低依次为髁体积、髁表面积、髁突长度、下颌支高度、髁突面积。以ICR患者处于静止期或进展期为因变量,选取RF中重要性排序前5位的变量进行分析,结果显示髁体积、髁表面积、髁突长度和髁截面积是ICR分期的影响因素（P<0.05）。受试者工作特征（receiver operating characteristic, ROC）曲线分析显示,髁体积、髁表面积、髁突长度、髁截面积和联合检测等均可诊断ICR分期（P<0.05）,曲线下面积（area under the curve, AUC）均>0.70、敏感度均>58.5%、特异度均≥64%。与单一指标相比,联合检测的AUC、敏感度、特异度显著增高。结论:基于ICR患者髁突CBCT特征的RF对疾病分期具有较高的诊断价值。髁体积、髁表面积、髁突长度和髁截面积是区分静止期与进展期的关键影像学指标,其中,髁体积的重要性最高。联合多项特征检测可显著提高诊断效能,表现为更高的AUC、敏感度及特异度。

关键词: 特发性髁突吸收, CBCT, 随机森林算法, 疾病分期, 诊断价值

Abstract: PURPOSE: To evaluate the diagnostic value of a random forest (RF) algorithm based on CBCT features of the condyle for staging idiopathic condylar resorption(ICR). METHODS: A total of 120 patients with ICR admitted to Nanjing Stomatological Hospital from January 2016 to December 2024 were selected and divided into the stationary phase group (n=70) and the progressive phase group (n=50). Healthy individuals who underwent physical examinations during the same period (n=40) were selected as the control group. The CBCT features of condylar length, inner 1/3 width, middle 1/3 width, outer 1/3 width, condylar height, mandibular ramus height, condylar surface angle, condylar axis angle, condylar volume, condylar surface area and condylar cross-sectional area were measured. Results: There were no significant differences in gender, age, height and disease duration among each group(P>0.05), while there were significant differences in CBCT characteristic indicators (P<0.05). Compared with the stationary group, the condylar length, condylar height, mandibular elevation height, condylar volume, condylar surface area and condylar cross-sectional area in the progressive group were significantly reduced(P<0.05). Taking the stationary or progressive stage of ICR patients as the dependent variable, the variables with significant differences in univariate analysis were included in the random forest (RF) algorithm, the order of importance from high to low was condylar volume, condylar surface area, condylar length, mandibular elevation height, condylar cross-sectional area, and condylar height. The above-mentioned CBCT features were subjected to stepwise RF,when the number of features was 5, the error rate of the out-of-pocket data was the lowest. The order of importance from high to low was condylar volume, condylar surface area, condylar length, mandibular elevation height and condylar area. Using the top 5 variables from the RF importance ranking for analysis, condylar volume, condylar surface area, condylar length and condylar cross-sectional area were identified as influencing factors for ICR staging (P<0.05). The receiver operating characteristic (ROC) curve analysis demonstrated that condylar volume, condylar surface area, condylar length, condylar cross-sectional area and their combination could all be used to diagnose ICR staging(P<0.05), with area under the curve(AUC)>0.70, sensitivity>58.5%, and specificity≥64%. Compared with a single indicator, the AUC, sensitivity and specificity of the combined detection were significantly increased. CONCLUSIONS: RF based on the CBCT features of the condyle of ICR patients has a high diagnostic value for disease staging. Condylar volume, condylar surface area, condylar length and condylar cross-sectional area are the key imaging indicators for differentiating the stationary phase from the progressive phase, among which condylar volume is of the highest importance. The combined detection of multiple features can significantly improve diagnostic efficiency, manifested as higher AUC, sensitivity and specificity.

Key words: Idiopathic condylar resorption, CBCT, Random forest, Staging, Diagnostic value

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