Abstract：
OBJECTIVE: To investigate soft-to-hard tissue response following mandibular reconstruction and to develop a predictive model for projecting soft tissue movement.
METHODS: In this retrospective study, 18 patients receiving mandibular reconstruction using a vascularized iliac flap were enrolled. Various indicators for characterizing the movement of tissues were considered to identify the effective predictors for projecting soft tissue movements. Face-region-specific linear regression models for prediction were constructed and evaluated.
RESULTS: The arithmetic mean of hard tissue movement in an extended area had the strongest correlation with the movement of the focal soft tissue, while the arithmetic mean in a regional area (Ram) was a more effective predictor. The linear regression model using Ram, global extrema and distances between them as the predictors performed the best in the lower margin of the face, with an average error of 1.51 ± 1.38 mm. Soft tissue movement in the alveolar process was not correlated with the existence of dentition, only can be predicted by the soft tissue movement below it. The area of the masseter was strongly correlation with Ram, but no other factors.
CONCLUSIONS: An accurate prediction of soft tissue movements in the lower margin and the alveolar process of the face can be achieved by considering hard tissue and adjacent soft tissue movements. No effective predictor in the masseter area was identified.
CONCLUSIONS: We investigated the relationship between hard tissue movements and the soft tissue responses in the facial area. Through building predictive models for projecting postoperative soft tissue movements, we derive insights for the aesthetic outcome of face surgeries.
BACKGROUND: This study was registered on the Chinese Clinical Trial Registry (registration number: ChiCTR2100054103).
METHODS: In this retrospective study, 18 patients receiving mandibular reconstruction using a vascularized iliac flap were enrolled. Various indicators for characterizing the movement of tissues were considered to identify the effective predictors for projecting soft tissue movements. Face-region-specific linear regression models for prediction were constructed and evaluated.
RESULTS: The arithmetic mean of hard tissue movement in an extended area had the strongest correlation with the movement of the focal soft tissue, while the arithmetic mean in a regional area (Ram) was a more effective predictor. The linear regression model using Ram, global extrema and distances between them as the predictors performed the best in the lower margin of the face, with an average error of 1.51 ± 1.38 mm. Soft tissue movement in the alveolar process was not correlated with the existence of dentition, only can be predicted by the soft tissue movement below it. The area of the masseter was strongly correlation with Ram, but no other factors.
CONCLUSIONS: An accurate prediction of soft tissue movements in the lower margin and the alveolar process of the face can be achieved by considering hard tissue and adjacent soft tissue movements. No effective predictor in the masseter area was identified.
CONCLUSIONS: We investigated the relationship between hard tissue movements and the soft tissue responses in the facial area. Through building predictive models for projecting postoperative soft tissue movements, we derive insights for the aesthetic outcome of face surgeries.
BACKGROUND: This study was registered on the Chinese Clinical Trial Registry (registration number: ChiCTR2100054103).
摘要：
目的：研究下颌骨重建后软到硬组织的反应，并建立预测软组织运动的预测模型。
方法：在这项回顾性研究中，纳入18例使用血管化皮瓣进行下颌骨重建的患者。考虑了用于表征组织运动的各种指标，以识别用于预测软组织运动的有效预测因子。构建并评估了用于预测的面部区域特定线性回归模型。
结果：延伸区域硬组织运动的算术平均值与局灶性软组织运动的相关性最强，而区域内的算术平均值(Ram)是更有效的预测指标。使用Ram的线性回归模型，全局极值和它们之间的距离，因为预测因子在面部的下边缘表现最好，平均误差为1.51±1.38mm。牙槽突软组织运动与牙列的存在无关,只能通过它下面的软组织运动来预测。咬肌的面积与公羊有很强的相关性,但没有其他因素。
结论：通过考虑硬组织和邻近的软组织运动，可以实现对面部下缘和肺泡突软组织运动的准确预测。没有确定咬肌区域的有效预测因子。
结论：我们研究了面部区域硬组织运动与软组织反应之间的关系。通过建立预测术后软组织运动的预测模型，我们获得了对面部手术美学结果的见解。
背景：本研究已在中国临床试验注册中心注册（注册号：ChiCTR2100054103）。
方法：在这项回顾性研究中，纳入18例使用血管化皮瓣进行下颌骨重建的患者。考虑了用于表征组织运动的各种指标，以识别用于预测软组织运动的有效预测因子。构建并评估了用于预测的面部区域特定线性回归模型。
结果：延伸区域硬组织运动的算术平均值与局灶性软组织运动的相关性最强，而区域内的算术平均值(Ram)是更有效的预测指标。使用Ram的线性回归模型，全局极值和它们之间的距离，因为预测因子在面部的下边缘表现最好，平均误差为1.51±1.38mm。牙槽突软组织运动与牙列的存在无关,只能通过它下面的软组织运动来预测。咬肌的面积与公羊有很强的相关性,但没有其他因素。
结论：通过考虑硬组织和邻近的软组织运动，可以实现对面部下缘和肺泡突软组织运动的准确预测。没有确定咬肌区域的有效预测因子。
结论：我们研究了面部区域硬组织运动与软组织反应之间的关系。通过建立预测术后软组织运动的预测模型，我们获得了对面部手术美学结果的见解。
背景：本研究已在中国临床试验注册中心注册（注册号：ChiCTR2100054103）。
