How tight is the relationship between the skeletal and soft-tissue facial profile: A geometric morphometric analysis of the facial outline.

Correlations between facial bony structures and soft facial features are fundamental for facial approximation methods The purpose of this study was to assess the strength of the association between craniofacial shape and the shape of the soft-tissue profile and to determine the extent to which it might be possible to predict the latter from the former. Soft-tissue and skeletal facial profile curves were extracted from 86 lateral head cephalograms of a recent Central European population (52 males and 34 females, aged between 19 and 43 years), divided into five parts, segmented automatically and evaluated using geometric morphometrics. The magnitude of the hard-soft shape association was assessed by principal component analysis and subsequent multiple linear regression (Halazonetis, 2007), by partial least square analysis (PLS) (Rohlf and Corti, 2000) and the RV coefficient (Klingenberg, 2009). The greatest amount of association between the skeletal contour and overlying soft tissues was exhibited by the region of the forehead (predictive power: 95.1%, RV=0.91, correlation for PLS1 r=0,96), followed by the region of the nasal root (predictive power: 40.2%, RV=0.42, rPLS1 =0.72) and the lower lip and chin (predictive power: 37.3%, RV=0.41, rPLS1 =0.65). The smallest statistically significant covariation was displayed by the upper lip and the maxilla (predictive power: 9.6%, RV=0.14, rPLS1 =0.43). The shape covariation between the nasal bridge and the tip and lateral border of the nasal aperture was found to be statistically insignificant (predictive power: 5.8%, RV=0.05, rPLS1 =0.26). Shape covariation was visualized and described by thin-plate spine grids. These findings correspond with the observation that the shape of the nasal profile and the upper lip contour are difficult to reconstruct or predict reliably in facial approximations. It seems that the shape of soft tissues might not follow underlying structures as closely as expected.