ABSTRACT
PURPOSE: To determine the survival rate of implants placed in different craniofacial locations and factors affecting survival. MATERIALS AND METHODS: This study retrospectively reviewed a consecutive series of patients treated at the Royal Melbourne Hospital who received craniofacial implants for an array of benign and malignant conditions. Implant survival per site and cumulative survival were determined. Surgical and implant variables were assessed using the Kaplan-Meier and Cox Proportional Hazards Models. RESULTS: Fifty-two patients had 156 implants placed to reconstruct the nose, orbit, and ears. A total of 43 implants failed (overall survival: 72.4%) in the orbital (29/63, failure rate: 46%) and auricular sites (14/70, failure rate: 17%). No implants failed in the nasal site (0/9). Three-, 5-, and 10-year cumulative survival was also determined. Independent risk factors for decreased survival included postoperative radiation therapy (P = .005, RR: 3.2, 95% CI: 1.4 to 7.0), implants placed in the orbit (P = .004, RR: 5.0, 95% CI: 1.6 to 15.2), and implants that were not loaded with a prosthesis (P = .007, RR: 2.7, 95% CI: 1.3 to 5.4). CONCLUSION: Failure rates varied according to site in this cohort of patients. A number of independent risk factors affecting implant survival in extraoral sites were identified.
Subject(s)
Dental Implantation, Endosseous , Dental Implants , Dental Prosthesis Retention , Adolescent , Adult , Aged , Child , Dental Prosthesis Retention/statistics & numerical data , Female , Follow-Up Studies , Humans , Male , Middle Aged , Nose , Osseointegration/physiology , Proportional Hazards Models , Prostheses and Implants , Retrospective Studies , Risk Factors , Treatment Outcome , Young AdultABSTRACT
PURPOSE: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. MATERIALS AND METHODS: 4D texture analysis was performed on DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). RESULTS: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with alpha = 0.05. The results show that an area under the ROC curve (A(z)) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. CONCLUSION: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.
