目的:面部裂痕是复杂的先天性异常,需要在彻底评估解剖结构的基础上进行综合治疗。本研究旨在使用几何形态计量学检查裂隙类型对颅面形态的影响。
方法:我们评估了75例双侧唇腭裂患者的侧位头颅造影,63例单侧唇腭裂患者,和76例孤立性腭裂患者。在16个硬组织界标坐标上进行了广义Procrustes分析。用主成分分析研究了形状变异性。在风险模型方法中,前9个主成分(PC)用于检查裂隙类型的影响。
结果:我们发现裂隙类型之间的平均形状存在统计学上的显着差异。双侧唇腭裂和孤立性腭裂之间的差异最大(平均0.026,P=0.0011)。裂隙类型之间的差异在PC4和PC5中最为明显(P=0.0001),它们一起占总形状变化的10%。PC4和PC5在上下表面的比例上显示出形状差异,下颌后高度,和下颌角。
结论:裂隙型对非综合征性口面部裂隙患者颅面形态变异性有统计学意义,但影响较弱,主要是在垂直维度。
结论:了解裂隙对颅面形态的影响对于为患者提供适合其特定需求的治疗至关重要。这项研究对文献有贡献,特别是由于我们的风险模型方法代替了预测模型。
OBJECTIVE: Orofacial clefts are complex congenital anomalies that call for comprehensive treatment based on a thorough assessment of the anatomy. This study aims to examine the effect of cleft type on craniofacial morphology using geometric morphometrics.
METHODS: We evaluated lateral cephalograms of 75 patients with bilateral cleft lip and palate, 63 patients with unilateral cleft lip and palate, and 76 patients with isolated cleft palate. Generalized Procrustes analysis was performed on 16 hard tissue landmark coordinates.
Shape variability was studied with principal component analysis. In a risk model approach, the first nine principal components (PC) were used to examine the effect of cleft type.
RESULTS: We found statistically significant differences in the mean
shape between cleft types. The difference is greatest between bilateral cleft lip and palate and isolated cleft palate (distance of means 0.026, P = 0.0011). Differences between cleft types are most pronounced for PC4 and PC5 (P = 0.0001), which together account for 10% of the total
shape variation. PC4 and PC5 show
shape differences in the ratio of the upper to the lower face, the posterior mandibular height, and the mandibular angle.
CONCLUSIONS: Cleft type has a statistically significant but weak effect on craniofacial morphological variability in patients with non-syndromic orofacial clefts, mainly in the vertical dimension.
CONCLUSIONS: Understanding the effects of clefts on craniofacial morphology is essential to providing patients with treatment tailored to their specific needs. This study contributes to the literature particularly due to our risk model approach in lieu of a prediction model.