Perspectives on craniofacial growth

This article underscores the importance of the growth process in the production of normal and abnormal craniofacial morphology. Although much has been learned over the past century, we still have only a limited appreciation of the 3D changes that occur in the skull during postnatal growth. We have also stressed that the current cooperation between reconstructive surgeons and radiologists is producing a substantive amount of data that could be used to further our understanding of the postnatal growth process. At The Johns Hopkins University we are putting our efforts toward the collection and organization of a large data base of craniofacial images that will allow us to study questions about the role growth plays in mediating the results of reconstructive surgery. Our ultimate goal is to provide information about the future appearance of craniofacial patients based on empirically derived growth patterns. In a recent article Dufresne and Richtsmeier' proposed that the results of surgical correction may be predicted based on the etiology of the craniofacial condition, and not necessarily the degree or character of the dysmorphology. The authors hypothesize that a categorization or classification of craniofacial dysmorphology can be proposed, based on the response of individuals to surgery, and that such a classification reflects real differences in etiology. Hence, a poor response to surgery reflects a condition that includes a growth disorder, whereas cases that respond positively to reconstructive surgery are those in which the growth process is not part of the dysmorphology. In the latter cases, a dysmorphic face is surgically transformed into an acceptable morphology, and normative growth vectors maintain or improve postoperative facial appearance. Thus, Dufresne and Richtsmeier suggest that this divergence in the response to surgery among patients relates directly in the role of the growth process in various types of dysmorphologies. Using our tools and the data base we are forming, we envision a markedly different preoperative consultation for future craniofacial patients. When a patient is evaluated, images from the data base with a similar diagnosis will be used to calculate a growth pattern for the proper age interval. The patient's scan is then 'grown' according to the appropriate growth pattern. The parents and patient can evaluate this new image and see what their child's skull will look like 2, 3, or 4 years from the present, both with and without reconstructive surgery. In addition, our tools will statistically compare the simulated (or 'grown') skull to samples of craniofacial images of normal and affected children of an age/sex/ethnic population that matches the patient. This testing will determine whether growth results in the patient becoming more normal, or more different from normal, with time. When growth patterns are currently considered in patient treatment, recommendations are made on the basis of what is known about normal growth, but a normal growth pattern may not be valid for all craniofacial patients. Understanding growth and its diverse roles in various disorders will enable the surgeon to use knowledge of specific growth patterns in the timing and design of the surgical management of particular craniofacial disorders. This will result in a closer fit between the patient's and surgeon's expectations of postoperative appearance and the actual postoperative result. Thus, the ability to simulate a child's growth based on an empirically derived growth pattern and then produce a computer representation of the child's future appearance could assist (1) the patient and his or her parents when deciding about undergoing reconstructive surgery, (2) the surgeon by providing him or her with a quantitatively based 3D projection of the child's future craniofacial morphology, and (3) the craniofacial biologist, who continually strives to understand the interplay of genetics, environment, and teratogens in the production of craniofacial morphology.