Three-dimensional facial simulation in bilateral sagittal split osteotomy: a validation study of 100 patients.

PURPOSE: Three-dimensional (3D) virtual planning of orthognathic surgery in combination with 3D soft tissue simulation allows the surgeon and the patient to assess the 3D soft tissue simulation. This study was conducted to validate the predictability of the mass tensor model soft tissue simulation algorithm combined with cone-beam computed tomographic (CBCT) imaging for patients who underwent mandibular advancement using a bilateral sagittal split osteotomy (BSSO). MATERIALS AND METHODS: One hundred patients were treated with a BSSO according to the Hunsuck modification. The pre- and postoperative CBCT scans were matched and the mandible was segmented and aligned. The 3D distance maps and 3D cephalometric analyses were used to calculate the differences between the soft tissue simulation and the actual postoperative results. Other study variables were age, gender, and amount of mandibular advancement or rotation. RESULTS: For the entire face, the mean absolute error was 0.9 ± 0.3 mm, the mean absolute 90th percentile was 1.9 mm, and for all 100 patients the absolute mean error was less than or equal to 2 mm. The subarea with the least accuracy was the lower lip area, with a mean absolute error of 1.2 ± 0.5 mm. No correlation could be found between the error of prediction and the amount of advancement or rotation of the mandible or age or gender of the patient. CONCLUSION: Overall, the soft tissue prediction algorithm combined with CBCT imaging is an accurate model for predicting soft tissue changes after mandibular advancement. Future studies will focus on validating the mass tensor model soft tissue algorithm for bimaxillary surgery.

Accuracy of three-dimensional soft tissue simulation in bimaxillary osteotomies.

The purpose of this study was to evaluate the accuracy of an algorithm based on the mass tensor model (MTM) for computerized 3D simulation of soft-tissue changes following bimaxillary osteotomy, and to identify patient and surgery-related factors that may affect the accuracy of the simulation. Sixty patients (mean age 26.0 years) who had undergone bimaxillary osteotomy, participated in this study. Cone beam CT scans were acquired pre- and one year postoperatively. The 3D rendered pre- and postoperative scans were matched. The maxilla and mandible were segmented and aligned to the postoperative position. 3D distance maps and cephalometric analyses were used to quantify the simulation error. The mean absolute error between the 3D simulation and the actual postoperative facial profile was 0.81 ± 0.22 mm for the face as a whole. The accuracy of the simulation (average absolute error ≤2 mm) for the whole face and for the upper lip, lower lip and chin subregions were 100%, 93%, 90% and 95%, respectively. The predictability was correlated with the magnitude of the maxillary and mandibular advancement, age and V-Y closure. It was concluded that the MTM-based soft tissue simulation for bimaxillary surgery was accurate for clinical use, though patients should be informed of possible variation in the predicted lip position.

A method to assess the clinical significance of unclassified variants in the BRCA1 and BRCA2 genes based on cancer family history.

INTRODUCTION: Unclassified variants (UVs) in the BRCA1/BRCA2 genes are a frequent problem in counseling breast cancer and/or ovarian cancer families. Information about cancer family history is usually available, but has rarely been used to evaluate UVs. The aim of the present study was to identify which is the best combination of clinical parameters that can predict whether a UV is deleterious, to be used for the classification of UVs. METHODS: We developed logistic regression models with the best combination of clinical features that distinguished a positive control of BRCA pathogenic variants (115 families) from a negative control population of BRCA variants initially classified as UVs and later considered neutral (38 families). RESULTS: The models included a combination of BRCAPRO scores, Myriad scores, number of ovarian cancers in the family, the age at diagnosis, and the number of persons with ovarian tumors and/or breast tumors. The areas under the receiver operating characteristic curves were respectively 0.935 and 0.836 for the BRCA1 and BRCA2 models. For each model, the minimum receiver operating characteristic distance (respectively 90% and 78% specificity for BRCA1 and BRCA2) was chosen as the cutoff value to predict which UVs are deleterious from a study population of 12 UVs, present in 59 Dutch families. The p.S1655F, p.R1699W, and p.R1699Q variants in BRCA1 and the p.Y2660D, p.R2784Q, and p.R3052W variants in BRCA2 are classified as deleterious according to our models. The predictions of the p.L246V variant in BRCA1 and of the p.Y42C, p.E462G, p.R2888C, and p.R3052Q variants in BRCA2 are in agreement with published information of them being neutral. The p.R2784W variant in BRCA2 remains uncertain. CONCLUSIONS: The present study shows that these developed models are useful to classify UVs in clinical genetic practice.