METHODS: A cross-sectional study of 252 AEA identified by computed tomography (CT) of the paranasal sinuses. The multiplanar CT images were acquired from SOMATOM® Definition AS+ and reconstructed to axial, coronal and sagittal view at 1 mm slice thickness.
RESULTS: 42.5% of AEA was within skull base (grade I), 20.2% at skull base (grade II) and 37.3% coursed freely below skull base (grade III). The prevalence of supraorbital ethmoid cell (SOEC) and suprabullar cell (SBC) was 29.8% and 48.0%. The position of AEA at skull base has significant association with SOEC (p
AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.
MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.
RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.
CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.
METHODS: In this pictorial review, we present six different scenarios of using 18F-FDG PET-CT in the management of suspicious pulmonary nodule or mass. The advantages and limitations of 18F-FDG PET-CT and Herder model are discussed.
RESULTS: 18F-FDG PET-CT with risk assessment using Herder model provides added value in characterising indeterminate pulmonary nodules. Besides, 18F-FDG PET-CT is valuable to guide the site of biopsy and provide accurate staging of lung cancer.
CONCLUSION: To further improve its diagnostic accuracy, careful history taking, and CT morphological evaluation should be taken into consideration when interpreting 18FFDG PET-CT findings in patients with these nodules.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.