Measurement of Thickness at the Inferior Border of the Mandible Using Computed Tomography Images: A Retrospective Study including 300 Japanese Cases.
Nobuhiro UedaMiki ZaizenYuichiro ImaiTadaaki KiritaPublished in: Tomography (Ann Arbor, Mich.) (2023)
Vascularised fibular free flaps are integral to reconstructive surgery for head and neck tumours. We investigated the morphological characteristics of the mandible to improve the incidence of plate-related complications after surgery. Using standard radiological software, thickness measurements of the inferior or posterior margin of the mandible were obtained from computed tomography images of 300 patients at seven sites: (1) mandibular symphysis, (2) midpoint between the mandibular symphysis and mental foramen, (3) mental foramen, (4) midpoint between the mental foramen and antegonial notch, (5) antegonial notch, (6) mandibular angular apex (gonion), and (7) neck lateral border of the dentate cartilage. Relationships between age, sex, height, weight, the number of remaining teeth in the mandible, and the thickness of each mandible were also investigated. Measurement point 1 had the largest median mandibular thickness (11.2 mm), and measurement point 6 had the smallest (5.4 mm). Females had thinner measurements than males at all points, with significant differences at points 1, 2, 3, 4, and 7 ( p < 0.001). Age and number of remaining teeth in the mandible did not correlate with mandibular thickness; however, height and weight correlated at all points except point 6. Thickness measurements obtained at the sites provide a practical reference for mandibular reconstruction. Choosing the fixation method based on the measured thickness of the mandible at each site allows for sound plating.
Keyphrases
- optical coherence tomography
- computed tomography
- cone beam computed tomography
- body mass index
- minimally invasive
- mental health
- magnetic resonance imaging
- deep learning
- risk factors
- physical activity
- cell proliferation
- positron emission tomography
- convolutional neural network
- coronary artery disease
- mass spectrometry