| 宋绍文,李晓静,王凤,郭会利,刘玉珂,张斌青.根据首诊影像学表现构建模型预测低级别膝关节软骨下功能不全性骨折进展[J].中国医学影像技术,2024,40(4):585~590 |
| 根据首诊影像学表现构建模型预测低级别膝关节软骨下功能不全性骨折进展 |
| Constructing model for predicting progression of low-grade subchondral insufficiency fracture of the knee based on the first imaging manifestations |
| 投稿时间：2023-12-05 修订日期：2024-02-27 |
| DOI：10.13929/j.issn.1003-3289.2024.04.023 |
| 中文关键词: 膝关节 软骨疾病 磁共振成像 X线透视检查 |
| 英文关键词:knee joint cartilage diseases magnetic resonance imaging fluoroscopy |
| 基金项目: |
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| 中文摘要: |
| 目的 根据膝关节软骨下功能不全性骨折(SIFK)首诊影像学因素构建模型,预测其进展。方法 回顾性分析60例低级别(1级或2级)SIFK患者,根据1年后SIFK分级将其分为进展组(进展为3级或4级,n=30)及无进展组(仍为1级或2级, n=30);比较2组首诊临床及影像学资料。根据组间差异有统计学意义的首诊影像学表现建立多因素logistic回归模型,预测低级别SIFK进展;绘制受试者工作特征曲线,计算曲线下面积(AUC),评价模型预测价值,并与单一预测因素进行比较。结果 组间胫骨内翻角、胫骨后倾角(PTS)、病变髁软骨损伤分级、内侧半月板挤压距离、内侧半月板后角根部损伤及撕裂类型差异均有统计学意义(P均＜0.05)。根据首诊影像学所见PTS、病变髁软骨损伤分级和内侧半月板挤压距离构建的预测低级别SIFK进展的回归模型为Logit(P)=－0.561＋0.300×PTS(°)＋1.702×病变髁软骨损伤分级＋0.874×内侧半月板挤压距离(mm),其AUC为0.962,高于任意单一预测因素(P均＜0.05)。结论 根据首诊影像学所见PTS、病变髁软骨损伤分级和内侧半月板挤压距离构建的回归模型预测低级别SIFK进展的价值良好。 |
| 英文摘要: |
| Objective To construct a model for predicting progression of low-grade subchondral insufficiency fractures of the knee (SIFK) based on imaging findings at the first visit. Methods Data of 60 low-grade SIFK patients were retrospectively analyzed. The patients were divided into progressed group (progressed to grade 3 or 4, n=30) and non-progressed group (still in grade 1 or 2, n=30) according to SIFK grades of follow-up 1 year later, and clinical data and imaging findings at the first visit were compared between groups. Then a multivariate logistic regression model was constructed based on factors being significantly different between groups for predicting progression of low-grade SIFK. The receiver operating characteristic curves were drawn, the area under the curve (AUC) was calculated to assess the predicting value of the model, which was compared with that of each predictive factor alone. Results Significant differences of the tibial varus angle, posterior tibial slope (PTS), grading of condyle cartilage injury, medial meniscus extrusion distance, also of the root injury and tear types of the medial meniscus were observed between groups (all P＜0.05). The multivariate logistic regression model established based on PTS, grading of condyle cartilage injury and medial meniscus extrusion distance at the first visit for predicting progression of low-grade SIFK was as follows: Logit(P)=－0.561＋0.300×PTS(°)＋1.702×grading (grading of condyle cartilage injury)＋0.874×distance (medial meniscus extrusion distance in mm), with AUC of 0.962, higher than that of each predictive factor alone (all P＜0.05). Conclusion The model established based on images findings of PTS, grading of condyle cartilage injury and medial meniscus extrusion distance at the first visit was valuable for predicting progression of low-grade SIFK. |
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